{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import requests\n",
    "import scipy.sparse as sps\n",
    "import sklearn.neighbors as nbrs\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.sparse as tsps\n",
    "import torch_sparse\n",
    "import torch_scatter as tsct\n",
    "import torch_geometric.nn as gnn\n",
    "import torch_geometric\n",
    "\n",
    "from surprise import Dataset, Reader\n",
    "from surprise.model_selection import train_test_split\n",
    "from surprise.prediction_algorithms.matrix_factorization import SVDpp\n",
    "from surprise.accuracy import mae\n",
    "\n",
    "if True:\n",
    "    torch.set_default_tensor_type('torch.cuda.FloatTensor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommender Systems Using Geometric Deep Learning\n",
    "## Recursive Graph Convolutional Neural Networks\n",
    "\n",
    "A novel paradigm, [geometric deep learning](http://geometricdeeplearning.com/), has recently emerged in the deep learning community.\n",
    "\n",
    "This will walk through the algorithm outlined in \"Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks\", by Monti, Bronstein, and Bresson.\n",
    "\n",
    "As we're probably familiar with already, standard Linear Neural Networks have an exponential explosion of parameters for high dimensional data.  It becomes infeasible to train even relatively shallow networks on a large input vector, not just for training time but for training samples as well.\n",
    "\n",
    "Linear:\n",
    "$$\n",
    "y = \\xi (\\sum_{l=1}^d w_l x_l + b)\n",
    "$$\n",
    "\n",
    "Recurrent Neural Networks solve this problem by remembering a history, allowing the network's number of parameters to be constant along the time or sequence dimension. Of course, this is most effective for sequenced data, with one \"sequential\" dimension.\n",
    "\n",
    "Convolutional Neural Networks solve this problem by sharing weights, allowing a static number of parameters across the image dimensions. Of course, this admits a specific data structure: images and things which are image-like. Locality of connections are understood in euclidean space.\n",
    "\n",
    "Convolutional:\n",
    "$$\n",
    "y_{c} = \\xi (\\sum_{l=1}^d ( w_{lc} \\ast x_l))\n",
    "$$\n",
    "\n",
    "Geometric Deep Learning, by contrast, takes in the concept of a convolutional neural network, and abstracts the convolution operator to work with a graph structure.\n",
    "\n",
    "For example, this example will utilize spectral convolutions, defned by:\n",
    "\n",
    "$$\n",
    "y_{c} = \\xi (\\sum_{l=1}^{d} \\Phi W_{lc} \\Phi^T x_l) = \\xi (\\sum_{l=1}^d ( W_{lc} \\circledast x_l))\n",
    "$$\n",
    "\n",
    "Where $\\Phi$ is called a *spectral transfer function*, analogous to the Fourier transform for sequential signals.\n",
    "\n",
    "For this paper, they use Chebychev Polynomials to smooth this operator, as normally it is quite unstable.\n",
    "\n",
    "smooth spectral transfer $\\tau$:\n",
    "$$\\begin{aligned}\n",
    "\\tau(\\Delta) f = \\Phi \\tau(\\Lambda) \\Phi^T f\n",
    "\\end{aligned}$$\n",
    "\n",
    "Chebychev polynomial:\n",
    "$$\\begin{aligned}\n",
    "\\tau_\\alpha (\\Delta) = \\sum_{l=0}^d \\alpha_d L\n",
    "\\end{aligned}$$\n",
    "\n",
    "where $\\alpha_d$ are the filter parameters for a filter $\\alpha$, d is the number of input channels (or size of the input vector), and L is the graph laplacian.\n",
    "\n",
    "Then a feature map can be calculated:\n",
    "\n",
    "$$\n",
    "y_n = \\xi(\\sum_{l=0}^d g_{\\alpha_d} L x_{n,d})\n",
    "$$\n",
    "\n",
    "The above figure along with the outline of backpropogation of this layer can be found in \"Convolutional Neural Networks on Graphs with Fast Spectral Filtering\" by Defferrard, Bresson, and Vanderghynst.\n",
    "\n",
    "\n",
    "\n",
    "### Recommender Systems\n",
    "\n",
    "Recommender systems are a common problem in business applications such as Amazon or Netflix, where we have a set of items, a set of users, and a set of user-ratings on items to connect them.  This generall takes the shape of a very sparse, massive, matrix.\n",
    "\n",
    "Using traditional deep learning methods can be computationally infeasible, and even after dimensionality reduction, it is difficult to frame a traditional deep learning method with training examples. It can require a lot of feature engineering just to create a trainable model.\n",
    "\n",
    "A common practice is to use singular value decomposition to simplify the problem:\n",
    "\n",
    "$$\n",
    "X = U \\Sigma V\n",
    "$$\n",
    "\n",
    "Then, a loss function is used to have this model predict some stashed away user/item ratings, essentially smoothing the matrix until it fills in predictions for any given value.\n",
    "\n",
    "Here, we will take this approach and allow a deep learning model to try and learn some hidden data using only X.\n",
    "\n",
    "We start by splitting the data into 3 parts, a training, validation, and test set.  The validation set sort of takes the place of \"training labels\", and will be used for evaluation of the loss function.\n",
    "\n",
    "\n",
    "#### The Data\n",
    "\n",
    "We'll take the Movielens 100k dataset, as it is an easy open-source dataset to use as a toy example, and won't take very long to train on. It consists of some ~100k ratings of ~1000 users and ~1000 movies.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading files...\n",
      "done!\n"
     ]
    }
   ],
   "source": [
    "items = ['u.item', 'ua.base', 'ua.test']\n",
    "print('downloading files...')\n",
    "for item in items:\n",
    "    if os.path.exists(item):\n",
    "        continue\n",
    "    url = 'http://files.grouplens.org/datasets/movielens/ml-100k/%s' % item\n",
    "    r = requests.get(url, stream=True)\n",
    "    with open(item, 'w') as fd:\n",
    "        for content in r.iter_content():\n",
    "            fd.write(str(content, encoding='latin1'))\n",
    "item_features = pd.read_csv('u.item', sep='|', header=None, names=[\n",
    "    'movie id', 'movie title', 'release date', 'video release date',\n",
    "    'IMDb URL', 'unknown', 'Action', 'Adventure', 'Animation',\n",
    "    'Children\\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', \n",
    "    'Fantasy', 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', \n",
    "    'Sci-Fi', 'Thriller', 'War', 'Western'])\n",
    "item_features.drop(columns=['video release date', 'IMDb URL'], inplace=True)\n",
    "print('done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie id</th>\n",
       "      <th>movie title</th>\n",
       "      <th>release date</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie id        movie title release date  unknown  Action  Adventure  \\\n",
       "0         1   Toy Story (1995)  01-Jan-1995        0       0          0   \n",
       "1         2   GoldenEye (1995)  01-Jan-1995        0       1          1   \n",
       "2         3  Four Rooms (1995)  01-Jan-1995        0       0          0   \n",
       "3         4  Get Shorty (1995)  01-Jan-1995        0       1          0   \n",
       "4         5     Copycat (1995)  01-Jan-1995        0       0          0   \n",
       "\n",
       "   Animation  Children's  Comedy  Crime  ...  Fantasy  Film-Noir  Horror  \\\n",
       "0          1           1       1      0  ...        0          0       0   \n",
       "1          0           0       0      0  ...        0          0       0   \n",
       "2          0           0       0      0  ...        0          0       0   \n",
       "3          0           0       1      0  ...        0          0       0   \n",
       "4          0           0       0      1  ...        0          0       0   \n",
       "\n",
       "   Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "0        0        0        0       0         0    0        0  \n",
       "1        0        0        0       0         1    0        0  \n",
       "2        0        0        0       0         1    0        0  \n",
       "3        0        0        0       0         0    0        0  \n",
       "4        0        0        0       0         1    0        0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,  94],\n",
       "       [  0, 239],\n",
       "       [  1, 116],\n",
       "       [  1, 981],\n",
       "       [  2, 197],\n",
       "       [  2, 614],\n",
       "       [  3,  73],\n",
       "       [  3, 597],\n",
       "       [  4,  10],\n",
       "       [  4, 347]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = item_features[['unknown', 'Action', 'Adventure', 'Animation',\n",
    "    'Children\\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', \n",
    "    'Fantasy', 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', \n",
    "    'Sci-Fi', 'Thriller', 'War', 'Western']]\n",
    "# algo = nbrs.NearestNeighbors(n_neighbors=11).fit(features)\n",
    "WA = nbrs.kneighbors_graph(features, n_neighbors=10)\n",
    "np.argwhere(WA[0:5].toarray() > 0)[:50:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('ua.base', sep=\"\\t\", names=['uid', 'iid', 'rating', 'time'])\n",
    "test_df = pd.read_csv('ua.test', sep='\\t', names=['uid', 'iid', 'rating', 'time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fields = ['uid', 'iid', 'rating']\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "train_set = Dataset.load_from_df(df=train_df[fields], reader=reader)\n",
    "full = train_set.build_full_trainset()\n",
    "i, j, data = zip(*full.all_ratings())\n",
    "i = list(i)\n",
    "j = list(j)\n",
    "full = sps.coo_matrix((data, (i, j)))\n",
    "test = Dataset.load_from_df(df=test_df[fields], reader=reader).build_full_trainset()\n",
    "train, validate = train_test_split(train_set, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "i, j, data = zip(*train.all_ratings())\n",
    "i = list(i)\n",
    "j = list(j)\n",
    "X = sps.coo_matrix((data, (i, j)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "i, j, data = zip(*validate)\n",
    "i = [int(ia) for ia in i]\n",
    "j = [int(ja) for ja in j]\n",
    "Y = sps.coo_matrix((data, (i, j)))\n",
    "X.resize(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0, 156],\n",
       "       [  0, 831],\n",
       "       [  1,  69],\n",
       "       [  1, 717],\n",
       "       [  2, 245],\n",
       "       [  2, 650],\n",
       "       [  3, 420],\n",
       "       [  3, 821]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HA = nbrs.kneighbors_graph(X, n_neighbors=10)\n",
    "np.argwhere(HA[0:5].toarray() > 0)[:40:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ff9a266db00>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADeCAYAAAAzWsvcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXl4FFW6/z+nqnpNdycBsi8sAiEbIQkYUBH1zqKOOoqAyOoGjogooo7K+HO4jooioIgI6qCoIIOIDONl1Jnr7iAiIjsBRGUnIJDO3umu+v1xqiqdEARUluuT7/PkSdd26q1TVe97zvddShiGQQta0IIWtOCXC+V0C9CCFrSgBS04uWhR9C1oQQta8AtHi6JvQQta0IJfOFoUfQta0IIW/MLRouhb0IIWtOAXjhZF34IWtKAFv3CcdkUvhLhYCFEqhNgqhLj3NJw/QwjxvhBioxBivRDidnN9KyHEv4QQW8z/8eZ6IYSYZsq7RghRdIrkVIUQq4QQb5nL7YUQy035/iaEcJrrXebyVnN7u1MgW5wQYqEQYpPZj73OpP4TQow17+06IcRrQgj36ew/IcRsIUSZEGJd1LoT7i8hxHBz/y1CiOEnWb5J5v1dI4R4UwgRF7XtPlO+UiHEb6PW/+zvdnOyRW27SwhhCCHamMtnRN+Z628z+2K9EOLxqPWnpu8Mwzhtf4AKfA10AJzAaiDnFMuQAhSZv/3AZiAHeBy411x/L/CY+ftS4J+AAHoCy0+RnHcC84C3zOUFwEDz90zgFvP3KGCm+Xsg8LdTINsc4CbztxOIO1P6D0gDvgE8Uf123ensP+B8oAhYF7XuhPoLaAVsM//Hm7/jT6J8vwE08/djUfLlmO+tC2hvvs/qyXq3m5PNXJ8BvAN8B7Q5w/ruQuDfgMtcTjzVfXfSXrDj7JRewDtRy/cB951mmf4O/BooBVLMdSlAqfl7FnBt1P72fidRpnTgf4GLgLfMB/dA1Itn96P5sPcyf2vmfuIkyhZAKlLRZP0Z0X9IRb/DfKk1s/9+e7r7D2jXRBmcUH8B1wKzotY32u/nlq/JtquAuebvRu+s1X8n891uTjZgIVAAfEuDoj8j+g45qPhVM/udsr473dSN9RJa2GmuOy0wp+mFwHIgyTCMPQDm/0Rzt9Mh85PAPYBuLrcGDhuGEW5GBls+c3u5uf/JQgdgP/CiSS29IISI4QzpP8MwdgFPANuBPcj+WMmZ038WTrS/Tue7cwNypMwPyHHK5BNCXAHsMgxjdZNNp102E52B3iYV+KEQoseplu90K3rRzLrTUpNBCOED3gDuMAwj+EO7NrPupMkshLgMKDMMY+VxynCq+1RDTlWfNQyjEKhCUg9Hw6nuv3jg98ipcSoQA1zyAzKcMc+kiaPJc1rkFEKMB8LAXGvVUeQ4JfIJIbzAeOD/Nbf5KDKcjnckHkkf3Q0sEEKIH5DjZ5fvdCv6nUhuzUI6sPtUCyGEcCCV/FzDMBaZq/cJIVLM7SlAmbn+VMt8LnCFEOJbYD6SvnkSiBNCaM3IYMtnbo8FDp5E+XYCOw3DWG4uL0Qq/jOl/34FfGMYxn7DMOqBRcA5nDn9Z+FE++uUvzum0/IyYLBhcgpngHxnIY34avMdSQe+FEIknwGyWdgJLDIkPkfOzNucSvlOt6JfAXQyIyCcSOfXklMpgGlZ/wpsNAxjStSmJYDljR+O5O6t9cNMj35PoNyacp8MGIZxn2EY6YZhtEP2z3uGYQwG3gf6HUU+S+5+5v4nbbRiGMZeYIcQIstc9V/ABs6Q/kNSNj2FEF7zXlvynRH9F4UT7a93gN8IIeLNWctvzHUnBUKIi4E/AlcYhlHdRO6BQkYrtQc6AZ9zit5twzDWGoaRaBhGO/Md2YkMrtjLGdJ3wGLkAA0hRGekg/UAp7Lvfi4HxE9wXFyKjHT5Ghh/Gs5/HnJatAb4yvy7FMnL/i+wxfzfytxfAM+Y8q4Fup9CWS+gIeqmg/lQbAVep8Gj7zaXt5rbO5wCuboBX5h9uBg5TT1j+g+YAGwC1gGvIKMcTlv/Aa8h/QX1SMV044/pLyRXvtX8u/4ky7cVyRtb78jMqP3Hm/KVApdErf/Z3+3mZGuy/VsanLFnSt85gVfN5+9L4KJT3XfCbLQFLWhBC1rwC8Xppm5a0IIWtKAFJxktir4FLWhBC37haFH0LWhBC1rwC0eLom9BC1rQgl84ToqiP9GCPEKIkSdDjp8LLfL9NJzJ8p3JskGLfD8VLfJJ/OyKXgihIkOaLkEW7blWCJFzjMPO6JtBi3w/FWeyfGeybNAi309Fi3ycnBH92cBWwzC2GYYRQmZz/v4knKcFLWhBC1pwHPjZ4+iFEP2Aiw3DuMlcHgqUGIYxusl+IzGtmeqOKe7cqSMuTaU+oqMIQVjX0RRph3YdrgED0uI9OFSFUCSCpiiEdR1LfCHAqcrjVUWgCEFE14kYBoYBmiKoC+uUBetIinWxt7yOzFYeW56wbrDrUA1tW3uJGIZ97vqIzuFDB6kRXlLj3QgEDlVuC0UiCASGWYbCOgZkIlooorOvXJ5PUxRURRDRDXt/p6oSikQa+sRsO6Lr6AYoAnQDnJpCRDfsdjXz/OGIjhCC/QcOEN+qFaoQRAwDp6qiGwaKEOiGgaoIwhGdHQdryGztta/L6jPdMOz+Dus6TlUFQDcMIrqBIiBiGLg0uT4UlrXVFAGaqthyKEIgBBiGPNbC998foFWr1va+1n9VUex7ZPWdYRjsPFRDapwHA3ktVpsWInrD+ax7byEU1u37o5vJIroBqiLs/lcVYe+3/8B+WrduYyWpHHEdVvuGIf9bx0tZ5Tm2f19NRiuPfV+sbQ3POmY/ikbtRW+3jom+FsOAsv37SUpMsPurqVzRx0Xv0xQ/dL6m7UVfY9P9msp34MB+2rRJOKLNiG6w41A1bVvFNCuPhabn+jE42nUA7N/fWL7jactqo7nrPd52jhf79+8nISHhRx+/cuXKA4ZhHLMB7Vg7/AgcV0EewzCeA54DiMvMMqovfZhaGsozRuOcVB/byioJhqEgPcDqnUHcCtQ22Tkr0UNpWQ0eAR2TfazdU3lEW36gGllbtwqZvmYhFgiaMngVeSXVEdlJfqDC3K8gzQ8CVu+sIBo+B+i6fCBq9MbnA/A5FSpDDUJ3SPCwbX9Noza6ZQT4akfjmmqv3tCDW+d9SX19hOoILBhZQrs2MZz9yHv4nQpaSCdakuwUL7sP1ZHZysu3B6t58YazGT77c/y1ETpkxLJ1fxWVtbJwY77Zt1Vhmb4XAvLT/PicDr767iA1uryhBvDKqF743Bq/mvIxyO7hzVG96DtjGU4Bxe1bM+HKHB5cvIG1uw6hR3QQglghqAzrPDmggNvmr+aVG3owZPYKCtL8rNlVgQHERD2J5ybLe2z19cs39mTUq18SDuvohsHKHYfxaNAlJRavy8HkAV2pC0eoC+v8asrH9MiIQ8dgS1klwTppSP1OwazrzuaWuat4+cYeXDrtUwrTAoS/ryJiQLAuggJ4HVCQ2Zrxv+vCY29vBmDawG6Mmf8Vk/rl85spH7L0zj4EPE5GzPmCUDhC+beHMNwqL4/sSVqcl7pwhHEL1hAO62iawn2XZnHtrGV0TolFUxSmDixg7HxZaFHTFB64rAvp8THc/MpKJvXLByDgcXL97M8p//YgT17fg2GzV5CdEsPMod25f9F6AJ4f3p26+ghj5n/FX67M4aLJH9E9I56Zw4qJ9TqprY9QVx/B5VC5+ZWVtpGdNbTYPm5Sv3zuXrgWgFlDi9m0p5wbXvqC/73rAlwOlbr6CGMXrGZi3zzuXbSOqQMK7PXRx1ttjl2wmqkDChi7YDUfmPu6HXKAUFsfwe1Q7f/l1SEumvwh/zuuj3z/vE77GbD2+SGUV4dwOVRGzf2SqQMKGDP/K3TdYPqgQlxRx46a+yUzBhcds73a+gg3vyJrB07ql8+9i9bZx9XWR+zzRMt5IvIe69w/5nghxHfHtd9JGNH3Av5sGMZvzeX7AAzDePRox7hSOhkpw588rvYzWjnZcTD0c4j6s8NBY8PRFCoQ+YHtP9SGpYQtTO2fz9jX19IpycOWfTVHHOvVoDrcsH92Sgwb91Thdwj++8o8xr6+1t4Wo0FaGw+b9zYYnQ4JbrbtrwUgK9lD6d4aYt0aHVp7WbWrwRDlpsawdU8VdQZ0TvKydV81BekBNu8NUmWe3+eA/3d5LveYCuqHrqs5LB7ViyHPLaMmAi4FIhGoM7cVZgTYsjdIZT3kp/j45kAV7RN9rN1VQcClEtF1quoNu29evaEHobDODS+vxC0gNyOW2vow6/dUkZ3sZePearqmB1izM2gb1GBtPeMXruPBK3O4dNqnnNuxtVR8b6yjuq6ezXuDpLf2ULqvBr9DoKoq7dt4+eZANW1be1i/p5KwbhCjgaIIuiTFsnl/BZ0T/Nxy4Vnc8PJKZg8r5vmPv2H19oPURuDdO3sz/o11VIZCrN9ThYYsGelRoWtaPJpDYcLvcxi/cB26YTDl2gLqwjoPLFpPOKIz4apcHly8ni1lFfxjzHkE3E7+8PJKHuqbS4LPLQ2nqfgn9svDpal8e6CKAc8tJz/Vx9SBhTz01iZ03eDBK7J5ZGkp91+axQPmPdR1A8wZXSgc4YXretiK36Wp1IUjuDSVP7y8kqcGdSNYW8+EJRt5pG8uf1q8gUn98kkMeNhxUA7GLp/2CfOiDOXt877ixRvPBqCuvuGtqQvL38HaevrN+A8LR51DwO3ApakEa0Pca/ZHOCyv7cUbz7aNXfT/6PZcmorLoRKsCdmzVsu4JQY81Jr776+o4U+LNzBraLGtlMur5dM7et4qnh/enWBNiMRAA1NQFqxptGwZJ0sWCyPmfMH0QYW2EbHaBZo1LBaEECsNw+j+gy8QJ0fRa8gaDf8F7EIW6BlkGMaRb7mJE1H0zZ4TcCpQ19x0AHAoUG9uS4/T2Hk43PyO/8ehAE4NasPQJdnHpr2VuMx+sf43NQDN4c+XZfHnt0rpmOhla1lD/aq8VD/fHaiiInSUjkbOWjAMKusbP1fWrOBY8jfXcm5qDOt3VzUyalabRRmxbNpbTlWUdVQFZKf6eHZwMXfOX82K7Yft/UXUOQTS0NQBMQ6BoRtUR8DnUqis0xGAzyGoMK/F71SoCevMu6mEm1/9knatPKza2WD0BJCX6kNRFNbuDJJvzlhmDi5k1NxVtpH3OwThiEFIb2z4z0p083VZLR4N8lLjWLXzMOFmOqRHppyxfLm93O5TBejRLp61Ow5RHdVojAYOh8a8ESVcNu1ThICijDg27Q2iKAqGoSOEgmEYBOsieFQImbPSRaN68ejSTZTuDfLSDWcz7K/LqajT8btUKuoi+JwKHRJjWL2zgqVjzuXhtzYR1nU27Q2CAR0SYli1M0iMClUR2ZeaqpCVHGBzWSWv/6En/Z5dRiQSoTIkCc2AWyUSjlAVhqVjzuXRpaWs3XWYSCSCoihU1Ebs+6cJiBgQcGvyOhB0TPRRuqecyrC8H/9jthEO62wqq6Bzgo/SfRUYhkFEj1AbAZ9TIyvRz8qdh+jRrhUzhxQDMGb+V/ZsLhzR2bC7nJy0WDmrMY3ZxU99QqfWMWw+IK/nt1M/Ztl9F5EY8LC1LMglT33Ce+POJ8Hvoa4+woVPfECnBB+lZRVkJwdwOlQ5U5z6EdlJAdswXTDpfSK6jqIofHj3hUdV9ser6H92Z6whP9YwGlkNbiOw4IeU/M9yTo6u5KFByQPHreQ7JLh5vG+uvXw0jiu9lbTKKuA6Cn/nFOA+Tm6vS0oM7qgZnAK4zbvUIdGNx9yWl+rH2aTNiX1zMRkZdh2SCtHSyVb/tE/04THb86jyZYjRwKtC+zbyYZryr60AxJgjDq9D4FHh2SFF5KXFUZAesOXokhKD1yHs9gxdnsh6LF1Kw34xGmQlxQDY1xj9+HZJ8ZGV7EEVchZhXf/63VUAbNlXg1eV/eF3CrzmCFk1+X2fU6GkXTzPDy1m3S45Uhx/Wba8Bg2KMmMpzIhl1uBChNmvdcD0gQXkpMRSp8t2z0qIwedUMIAOSX5A9lPnZD8vXdeDm19ZSeckH05FJcYht2WnxFCQEWDt7krW7AxSkB5AUQQGcO+b63hjVC8KMwK2oanRIS/NT1Gm/elVvimrRUEaak1VmHdTCQGXQsCt4XMKYjQoTA8wZWABW8sq8TkVCtL8xLo1erSL5+G+eTidGvlpfhSgKDOOnNQ4slMCPPw/m8hN9VGUIc9XEzaYc0MPspNj6ZLsJzslgM8BdRGYc30PijPjuP7FL9i0N0hENxg++3MiEZ2AW6VTkh+vJvnqbWWVqAImLNlgK/nOSX6EIth2oIrcVB+aQ7492Smx/GPMeTw3rDtdkv1MWLKRrCQ/OalxnN0unh5t48hK9KNpGrFujUeXljJ5QFeykwNomsacG87G71aJdWuUtItnyW3nEud18MaoXuSmxSGEYOv+SlRN7tMtPcCg55cTCkfQNIXX/9ATp0MlOzVAl5QADoeDt+/ozYf3XMicESW8f1cfNFVhzPyv7HvicqjMGlrMX6/rwQd3X8jUAQX89smP6TPpfca9voYFN5fgdmu8c0dvEnxuAh45uyivDnHNrOX8/dZz+NPiDYyY8wUuh8rbd/SWMqQE0FSFaQO7EfA4yUr0s6msgrr6CLFeJ++MPZ/89Hhy02Ibjfx/LM6IombHGtFntHKw46Acsh3PVD81TrD7sOXwhNCx+JImOJ5zdE72snlv9RHrf64ZQ5fkGLbtrbLlePj32Yz/+0Z7e26Kn/V7KuicFMPmfVIRWvREfqqPh67MZ+gLnxEOG7a/ACDGqVAVNSL3qqCojX0H7ds4+eZA6IgR9mJzhLd+12Eq6w1cCnRNi2PVjsOEaX7U3inRy5ayarwOQbU5MlaADkketkaNzj0a1JjdlpXsYeeBGqrCcjQfcLsY+6tOPPbPTWzcU051GMZf0omH/7mFksx4HrxSRu8+/D+buKVPB0a/tor6+jBv39mHy6Z9QrA2QowDOiXH8tWOhlGw36mAEBh6hOwUqQBvueAsRryyEq9DQdd1plxTyM1zVzFrcCH3vLGW8towPTLjeLRfPuMXrmPjvnKCphXtYSrtCVfm8tBbG1m27SAK8JY1qtR11u08RHW9QedkL628HiZencdl0z7hmUFFPPvRNu7+TWeue3EFC2/pRb9nl9G+tZdJAwq4e8FqVu0MEutSeev287hrwRo27ZUj1GeGFuHSVEbPW0U4ovPQVbk8sHg9j/fL54rp/2HJ6HO482+rWfHdYYozY3FrGhOuzGHCko2s211OTnKAif3yuHP+ajaXVWIYOtlJsTwxsCsVtWGufe4zOicH2LKvgoW39GL8m+tY/s0hCjMCzBranbpwhHvfWAcG6BjMHFJMXTjC7sM19Ju5jCWjz+WhtzbyeL98/rR4A9MGdmP0vFVMHtCVca+vQY8YNn1098K1Nv1j8fu19RGCNaEjtgGNKJBo6sXyk4TCEdsv0tTHANjHRvsRrOWj8ebRtEo0xWNti6ZfYr1OyqtDjJn/lU351EZRR9ay5Z+JpnksH8vPQd2cEZmxnmNYLEvJw7EVMMDuwwaaOdpVdEjynZg80edoOmq20JyShx+eMTiO49zWzGFTlJL3u1T+O0rJA9zSp4NsM+oOWr2kqQrD//o5II5wWGe2cje66Z1TY1GEVLQWvjkgz9x0kjTk+c/YuKcc1YzKqdPhUF0I64p9LpX8tMadvaWsWtIB4QYT0CkpppGSB1CUhmfgm701Nr//3f4qHrgsm5Evf4GOYc9QHv7nFgBuvqADV0z/lAffXE9NfZghs1eQEuukJgx7DtfSJTkASCpi2/4qe3YE0Dk5QIeEGCrrYeOecjbtDXLDyyt5fmgxYFAVhlHzVhGjwcvLt9M5yWf3Xb9nl7GprIL2CTF2e7phoGkKjy4t5fF++fidCj6XglNTUBTBHy/OomtGKwoyYtm4p5pQfcRWTLfN/4oJV+Qw+d0t5KbGEnA70HWdVTuD/HHhGr75vprZw4rJSY3lntfXoqkKHdp4WblD0lIuh2pb2n7PLkNRBC5NJS8tlgS/h2cGF1HSPp5nhxSjaQoJPjfTBxXywV0XMHVgAfe/uZ4t+yt5ZlAhQgg27i3nnoVrpZJP9PPMoEI+vPtC0uNjeG5od3pkxvHt9zW4NJUEv8fm+q3oqbHzV3P9S19QmB5HhwQ/M4cU86fFGxpFYwU8Tib37wrAFdP/w7gFa5g2sBuJAQ+xXqetZN0OlYDHia4bBDxOYr1O6kznaF1YKsm6+gj3LlqHy6ES63WSGPAwfVAhToeKoggCHiczBhfZx1qj9ptfWcnNr6xspODdTZR/U8R6nfbf1AHSgFjHRytl63es19mI17fOEb08a2hxIyVvYeyC1Y0Mw4/FGaHoQ80RkVFwNZHyWDRI58QYLL1Sa8C+I4Nvjomp/WX0Q6jJEDXZJ0+elxqw11k6MidFKjlLPI8qlZ8F51GshiNqtcvZ+GLz03y0b+OznY8WZn30NQJQo0L6Opu0iIJAKIKOST7ObhfPqzf0wG924uZ91UyMoqQMQ6d9go+asKRwYkxhXIqMvonui9nXn01BZisevrLh+D0HGwxeZis3TkVl8ahe+ByCGIegIF3ykM8NLbb7ZW95HbMGFwKSylFM2WcPK6ZbRoCCtnFSFqdCblo8D/1jIwaCbWVVzLuphBitoV8z23jJTwuw5UAlDkXB71TYUx7C61QY8Nxyxv2ms03tvTayhKIOremS4kUVAAbffV9N97ZxZCUH6JzkRxUw64NtPDtEDpJeG1GCw6ExdUABLwzvQY928TwzpIh3x55P14w4Jg/oht+pEHCpPNa/qx1C6dJUVFVF13X6z/yMsb/qyFUzljH+d1148bqzKWkfj9upEfA4+dedfXjnjt5MWCKN+YzBRSQGPCy69VwU8352SvQx4uWVhA2DTfsqGP+7LnzzfTV+t4pLk0pj+rWFzBxWTH56nB0lM/1a2c93L1zLc0O7kxjw2LyzpezuXbSOyf27kpXk57qXvqBjgg+hCB66MhchBFv2VzFuwRrqwlK5uhwqr4zoyQd3XWAr5GkDu/GnxRvsZ0HTFJaMPgevWw5vLGX3/LDujUbj9y5ax4whRXxw1wU8P7z7Ec7IaCWnmGGYtVGRQHcvXItuGLgc6hGRNbFeJ88P687zw7vbirXWVPK6bti0TLQSPlFYyv5Yx/+Y7e5mrunH4oxQ9O3aeBstp8dJ1WnpvGj+vXNSDLWG5MM7JTYc5wLS4+VD5XQICtMDxDiEraR+CAqNueJOSW5eXvYdHuVI3n1vpdT8t114lr0ujOR7HYrA71TokizlapfgZebgIoozYilMDzBrSPdGo3rLQNQb0C7BKbfpDRfbOdmLW9WYeHUeXg26pvvtbUN7tkVVBA6h4DUbcqimsAKeG1rM6h1BasMRbnn1Cwwz6qNbRhyPvr2ZzDZSkq37Klm9M0hOqg/DgKp6A7cq+/yGc9oBMH7RWmYPK6ZdmxgevDybMfNX8/DvJfctogKLI8DKHeUcrJQvqTAMtu4NEolEuONvqwDITYmhXWsPL3zyDQDb91cxuX8+q3cEGfu3r3AIGcVRG5Z9cdtFHVGEYM4NPQhHIjz8PxuoCsPMwUUsGFlCwO1g9Y4g80aU8PSgQvLS4pg3ooSCjHiWjjkXn1t2zrybSvC7NSZckcOmPdXkpwdwaxovXd+DiVfns3JHOYoQ5KT42FQWZMjsFYDMYTjLjLwJ1obYtDfI7fO+wqWpPHJVLg8u3kBmaw+dEn1M+MdG9IgM70sMeHjp+u6oqsqS0efQvo2Pf93Zm0eXlhKsDfFE/65MHtCVYE2IcQvWEKytR1GEHSoKEHA78Ls1NEXh0avz6dmxNS8M687rf+jJo0tLyU4O8NaY8xrRA3XhCA9ens2fFm9gYt88oHHUCkjlNG1gN2YNlWGYMwYXEfA4mXNjCUtGn8OT13ZDCIFLU3h37Pl8ePcFPNI3l3EL1jCxb56tNF1NlOq0gd14frg0kH+5MoeMVr5mR7IWXA6ViX3z7NFxdCTLhU98QFmwhlFzv7SV/rSB3ew2LGM4bWA3nh8mFXmwpsE4WMc0PadllKKVf/T2Hxo919ZHjthuGZ2fY9TdXBs/h5KHM4Sj96R2MpKGHcnRW1y5RxPUhI9fTreA9kkyVO5YcABd0nys3fUjhv3NwOuA6mbiI48n6sSCS0Bd1M4xGlSFYfawYm54Wcb5qsDzw4p55r2trNxZ3uh4jwCnS5PRDE2iXxaMLKEsWMtoM467OdlUIY1sTZPnTlOgIC2WVTvKbVrHo9DIB9Bcez6HuVYI2xfQ1OfgUaBzip/S3RXUGtAx0cPWMknveBVQNUFF0+mVeT2DXvic4nTJj6/ddQiHQyWzlQev5mD59kON5MxL87NuV4V0PifJaJ5uGZK7z0/zs3ZXBd0yYvlmfyXtE2LYsjtIVaThPkQi4HKp5KTEsnFPkPpw2KaZFo/qxbC/Lud/bu8NQJ/HPyTGqfDm6HPp/+wy5o4o4ZqZ/6G6Xibx+ZwKQhEYBlTURXhzVC8mvbuZT7d+z8f39OHehetsR+KGXYd56/beBNxOLpj0Ph0T5OzR7dSYPqiQ0fNWEaqPsGlfkMq6CEUZcShCUFpWQX5aHI/0zeWu+Wt47rruduy5NVq0jMRfrszhoic+oigzDl032Hqgii5Jfh7vn8+lT36Eoih0zYhn+rWFkpePijMvrw4xdsFq7r80i/7Pfkawtp53xvamY2LDzLdpXP+kfvlc/OTHvH1Hb1yaao/ma+sj3PjSCmYMLgJk6CLIEX204bC47VlDiwnWhDhn4nu8f1cf7l+0nvV7grx9+3lHcN5Ao3j4aB7euobo2PnobdYMqOkM4KfG0FttHG+8fzT+T3H0TpN+0JqMni2O2oicmDGqNbCV/NE+tW6hHtjYjJL3OI7BDzUDB1BTDxnxR96o6JlBetwPs/V1TS7XUiS3vrrSXpefHssNL69kc1lDqpQVyaI5FDon+RBCOlujMeC55Y2UPBzph/jvK7JRFeiQ6LLXCWDkcevcAAAgAElEQVThH3qhCIHXITDzyXj+uh72Pm1bOwi4FGIcwr7enBQfnZICcuRvGDJk0anwp79v5I+/kbMiBZn8tmaXVPJdkmNsJW/1xys39aRbRmwjObNTvDz93tcs/ENPADbsLac6AjW1EdburqQqUi8droCqymS0TXsqcKsyEW797ioEEDFH0IqQsriEwltjzsMhFGoi0DUtQEG6VFi1BrRr7eXx/vlkpwSYO7IXfqdCfpqfR5duIlinc9m0T7lnwVp8LsnNP7BoPbphMHDWMoRQ6JYRywvDihGK4LWRPclODqAIuO7FFdz9m86A9C+s232YTXvL+ePFWVTU6dyzYC3B2hAdE3yU7guyamc5IWsUKKQizEmNlUpeEWwuqyQryc/kAV25c/5qlm8/xMhXvqCuPmIrUZvKMAwS/B7+ecd5aKqCpsgolU37KrjztdVUhAxeG9mTyf27Mvq1Vfx26kdMuCK7kaGYcEU218xaztwRZ1OUGceAmZ81omCun/05fR5/j7r6CLOGFhPwOOmU6GPs/NX0mfR+IyenIkQjBT99UOERCjZ6phLwOCnp0JoEv4fnh3fn77f2asSdW4oUsBO6yqtDNj9vjcynDiigzlweNffLRtusWVBTRdycYj7REf7PSdM0hzNC0deaHP3RBu21J6DnLWebRYukxoljjqSbc58mxh5dGR9tSz1yJLvjkOnRj+pdAcSYGnXn4cZD/mja6IduiBANBsNyxqbER41YzCxeXdeZ8PtcdN1ANJNe3jGxMVXW1LDMW76dynrYVtbgGZAOVZ2V2w/TNsFLp2QvbgUmv1tq7/Pd9/VktvFSWW/YbW7bW8mqnUHqQjqV9QZdUrxM7l+AATz27teAdPoaQHaq9DGMvqAjIGctAJ2SvTy2tNRWyCB9Cdv3VxMKRRj6wmds3BckYtJe7ZM8+ByC7Qdq6JjoIyvJg6qqPNG/gJeu64GZLMv0gQUy7t/E7Rd1xusQaJrC95UhSvcFKcgI4FAEX+8L0iklgCpg8jXd5KhxdznXzlxGRhsPa3dVcN+lXShpF88bo3rx+IB8ctPi6Jzo5/EB+eSkxJKdEktWkp9Nu8u54eWVhOsjTFi8gSeu6UpRRhwLb+lFa5+TpWPO5eZXVnJWop+KkMGEv68nxqlQGwlz2bRPKN0XpFOin4VmuGCwNsSDl2eDAU8N7CZLTIR1Xr+lJ05NcvhOTSU31cfDV+UxZv5XBGtCdhborKHFNpdvGaWN+4Ik+Ny8c0dvXri+B0vHnEuHBD93L1zLI1fl0jnJz/2L1kt6xcxaTvB7+Oft5/HY25txOzVevelse5ReVx9hw55yEIq9XFcfYXNZJROuzEGPoizdDpXJA6STti4csSkmt0OlLCgHAJby/cuVObZynDqgwG7j/kXrj+DOLUUa63XatFb0OS3jd9HkD6mrj9jtWdt+KPolGtFG4kRwspQ8nCHUzU9NmPrJ51exX/5oWNmIPxfatXHz7YHaH328WzQYveZKQEQjJ8XHhj2VjcIWm2sHGrJmjydztylVU5DmZ8u+SqpNK/1T+izgUqmvjxxBBTWVWQA5KTGs31PF7GHFjHhlJXmpflbvqrDLSuSn+ti2v9JOpHIhKZeKuogdy25RTFYYqWom4ERTZLFujWBtWCbzOBVeuK4HZ3dow46DlVwy9aNG1FiPTHMkva8S3dDpnBxgxbeHKGkbL+sOYaBHDLYcqCItzsn6PVUoQHG7eL787hAeFSrDkgKa9M5mM/TyUwxDxwBq6nUK0+PQgVXbDxPwOOiUEMOqnYcxDBlZ9O6dvek7/VOCIZ0ebeNwqiozhhRx05wVrPjuMAGXSm5KLJpDQY8Y/PX6HnbpgvsvzeK3Uz/mnbG9efDvG5h6TQHjFqyRin9vBX+/tRf3L1pPOKyzcV9QzkQUwfrd5eSmxfLX4T3sEX6wNsRFkz+yk4UARrz8BY9clUvA7eSiyR/yz9vPY9zra3jkqlwufOIj/nPvhSQGPPYMIRSOULq3gvdNp++Og5VcNPkjPv2j3K8sWMMlT33CYlOuNTsPkZcax8R+efz+mWW8P66PTc9E0yJlwRoufuoTclMCTB7Q9YhoF2tm0ZSqORF65eegc44Hpy0z9scgNjPLiB805aS07RRHRs6c0PEcGdLpdQh03aD2J/pfOiS62VZWayucDm08bDtQc8R+FmdvZbuCjC/ffqCG5Dg3W8zsVctg/fmyLKb8awvBOp3clBi27q1qNGpXgLx0P2HdYPt+SV9Uhw1+iCETQHczfvzKpz+xFdyCkSXc8OJyKusldVQbAbeZnRuNaD9LUw7fiv+fNVjGrBemB6gKRdhcVsWCkSW2Q/XSaZ8CMvmpMLM1w3plMm/FTmpDYZ4Y0JXfPfUxFXU6PqdCx0Qfq3YG8Zh5ArX1OnmpMlM12zSCIJV7XpofDLj7t10YNnsFxW3j2LQniBCCRbeeA8DVM5bRISGGtTvLef/uPvxp8QYqa0KU7pPUmaHLgmL5mfFM7JvH/W+u58HLs/nt1I/p3jYedNhUVgFICsQKW1QUwdOD5Gi6LhyhojbMkBc+JyvZb8ejA4x7fQ0PXp5Ngs/NmPlf8cBlXUjwue3UfQsBj5NgTYjbXlvF09cW2nVogjUhfjX5A+bf3IsOCf5GtWruXbSOiX3zGPf6GtbsOMS/7uzD2L+ttovizRhcJGcNSzbK/RasQcdg6oACAh6nTaE0VapWyQCgUX0d63c0R77jYCUZrXyNaspY8e7R7U64IpuMVtI/UVsfYX9FDQ8u2ciEK7K5Yvp/6Jzgw+3S+MuVOST4Pc3W2Rk190t7RB/tZ7AQvU/A4/xBPv501bix8H+Koz8e2NP4xJgf3M+ClbkZMhqO/SFYPLVTwY5icQvQmkuJNQzqIg1RQda5vMfg9dPiG7rbAewxR/fp8XJKuO1ATaM476ykGMwoQAC+3d/gS3AKM5JBazjASgyb8FYpnRL90hh8X0Od0UA3eRTomhFgzc4KNuyuxDAMVE1jzvUNXLvP5JwttG3jpCgjlikDC0jwuclOiZXZoMlenvr3VjolBfA5BU8NLEQBXhjWgxgNOwMXoGNiDDEOOXv428gSe5tTNMT/371wNYUZsUwbVIjLjCAKhXX8bo2H/rHRflirw3Djee24ee4qbr2gA05NJeB2sujWcxHAKzeV4HM7mT2smJqIDPt8bUSJrIQJ+J0Ouqb7KUwPUJQZx7ayKlbvquDWuV9SkBHAqal0SQ6QnRLgobc24dIUXhtZgtehUZgRR8Dt5C9X5rClrIKzEmIQZsy9gaQPMlr5mNy/K+nxMZzdvhVPDOgqa9wk+8lJicXv1hBCMGVgAZqq2FE3v5/+H9r4XLx9R28UIRj16pfcvXAtdeEI068t5EFTaU4b2I1Hlpayv1I+PwGPjBt3adLJ6tJU5txQQsDjtCNVJASDX/jcdohOG9iNuxeutRX29Gtl1BJIjnzygK5oqkJdOMKfFm9g6oACEgMeJg/oytQBBXYxNGgc721RIAl+GRVjydA0SgewZwAPLtloK73owmHRCU0zBhfZswPLEWvJldHKxzt39Mbt0pjUL5/7F60/Ij7eghVlZCVQNZfEZMXHN0VTJR99jhPFTz3+RHBGKPpas0ZB5yTvUfexusJ9FGXa9EKMqGn+8XSjNeoP6Q3OW0Vpvi5MTRjOSvTa+7Y1jU91kwiX6FIGTgV2HWrgJOppoEB2HGoYkUXTMTu/r8KjNRTwSmnVwBGW7gtSEdJZv7vSvs7opKewrrNxdxWGodvnwzynHiWmqqrMG3E2z7z/tZ2foCgKzqiSy98dCPHljnJ+99TH/OHVlSiKQCAd3lX19Xy1M0hlyODmuatwCrh13iqp+Ax5X7wqfHugmqp62LinihtfWmFfu9XvBWl+spIDfHOgirsWrGGjmfwwfPYKzn/8Q3QM2/hkJ3t58T/fUZQZy4g5K6gNhW3HndcheGxpKaFQhOc//gafS2Xj3mqufW45G/eUE+NUuO93XVizs4K1u4OM/102qqrQNd1Pl5QAX5dVMv53XdiyvxJNlZUlL5j0Ib+b9ikhXWdzWQWj5n7JXfPXUF2vs2ZXBSH7vsv/5dUhLn7yY/ZX1KApCve+sU7y02ZZ4/sXraeTGTWjKIJQKEK/Z5dxVpsY7l64FpemEqqPmElWnblg0oeU7g2y/NuDdpLQ/Zdm8aspHzP8r8sZMecLO2JkYl/JwdeZSmT0vFWUV8sQTkURLBktZyiWY9KCtawIwbjX1wDSgFjVKcNhHZc58r74yY8Zu2A14bDeqGKl1U5dfYQRc77gxpdWMHreKsbM/8pWZrOGFjc6b3RMvKVwxy5YTVmw5ohkJqt9yyBYMfCWMQh4nLaTV1GEbWCac8hG8+7W+qaFxH4O5+iJKPGTqfDPCEVvZcbWR5onnV2iYURaE26+M5oeaSnMTslHNx7RiL6dtWE5qo1EGgpfRTtgH+ubaxf7cgDf7q2yt3VOiqFdgguP0rhzQ/qRiV8WMlo3tN4pyW1HytSGpaHJSvbgEvDNfvkgZrRy4tBUPIpMHGqf4MIlohK1NPjmQBV+t8q0axu/WG7R+Fon9evKQ0s2smbHQZsDf+TKXDSh4FUhK9lLjAM6J3kAgR42uO/SLrRPdAPynJZBG39JJ2oN+PPlMsa+1pCZu9LPZtAxSY7G2ifG2CN6rwpdUrxSCeoG80aU8OAVOTw3pBivKkNIizJiefiqPJyKSudkL6V7q7nv0iwM3UA3DEr3VVBZE+Kq6Z9SWW9QEwmzcV85esTgn7efx9T++eSl+RFCYBg6D7wpR6LPXFvIY0tLeen6HmzYXcHN53egok7nwcVSEU+8Oo/0+Bi6pgXomubn0b55dM2IZ+o1BTidKktGn8vTAwuoM6Bjkoc3R59nj3Kzkv3cs2At0wcVMvWaAgwMdN1g455ybj6/PSu2H+aypz7hvkuyQEBGvBunJkfZwdoQK7YfZtxvOvHgm+uJcamMfPkLlow+h4lX53Hxkx+T4HPz8T19eMJ0WrocKhOuyCbgcRIyp3YPXNbFTgyaPqiQrERpKMfM/4qJffNwOWRBLZc5ggWYPqiQ6dcW2jH+VtmBkEmTXD7tExb8oSczBhXJ7FuHyv2XZtnOUms0b30P4ZG+uTZVYxmBaKUarAkdkV1qLU/ql99oRhLdvhWqGd2elSFrGQFrv1Fzv6QsWHNEZEu0EbAicY4VR9/oXTLPE20MoqOMmjMgTY+PNkY/xoF7vDgjOPrT7YxtDscqOdwcVGT1yKbOz+b2+6m306p02FxNe5Dc+cP/s4HVuyqO2NY0hr1pnXxo/vp9TgUBjapXdkp0s6WssYO5uZyBzNYa27//8a5trwLVeoNsOal+Vu+uoCA9wNf7gtSEwa0JquoNvKqMUBLAWVG17V3QKMPYciwXZ8QSNnTW7qxAQ2YnV4R0Ai6Fl28s4coZywBZdfLZod3p3r41w19Yzvpdh6iMuqT8VB8+p5PHB+Rzz8K1fP7NQd4Z25vK2jBXzVhGXpqPdbsqMZDPgAzhNag0O7o4M5YnB3ZrqC+T4GfFjsMUm2GlCgKnU6U2FObZocW4NJU+k94nOynAQ31zufjJj5l3UwkDn1tOcWYcK7cfxudS+fe4PgRr6/nVlI+J8zjISvLh1OQHZzbsCdIlUX5fYdO+Cjq38ZkO1sMIRZCXGsctF3RgyOwV5Js5CD07tOJxkx6579IsfjftU/51Z2/S4+XMdsScLwhHdB7vn88VT3/KktvO5f5F623HZ6O4fdO5CnJEfvMrK5k2sBvQ4AydNrBbo4zZMfO/IhSKsHFfkNyUWGYMKcLlULnxpRV2vf1gTcisHNmHunDE/t205n105BE0rnkT7XhtbtminaJls5zMFvdvzXiamx0012a0DMeDXxxH/2PxYydeJ6rkwaxKeBy67Oew2W7TedCckgd47O3SZpU80EjJA2S28TQKM4Qjr19y8gGeHVKML4o+i1by1tqO5mg/Gs0p+aM9fOMv6WT/zkn1UZgeQI1KsqgHtuyrwO9UeLRvPlnJscy5vgfV9YYswxyRuQdSCTc4gKOVvFuRGckAm/cG2VZWidshCAG1piGLRHQeWiJ5cY8G9WGDIbNXsGbHITbuDVIZbjxLW7u7knW7D3H505/yeL98umXE0veZT7lyxjIMYOOeBiX//LBis2aQrEqpCnjgshz6PP4hl077lM6JfsabM6OR53fgyx3lrNhxmIq6elbuKOfXUz4kWBuS+QkCHvz7BgrT4xj58kp8LpUpAwsozpQVHW+f9xVXm8bqpeu743ZqTB7Qlb9e14N37uiN5lBQhGDuTWez+UAl4YhORUiXMlzWhevnfMGswYXsOFhDt4wA4bDO5U9/SljXeeztzbw5qhcPvbXJHlHrZpkGq/fveX0t63aVM3a+HDFbVEuC32Mr+Uue+oRgTYhwWG8U7WKVa4iOZ5/ULx9NU8hK8tuzCsCuBgnSyfrP28+z6940VfJAI/qnOeXadPTfVOmXBWu48IkP7BF7rNfJe+P62HV1rFDOo1FAzcXOn6xR/Rmt6C3hji96tXlYXXai6U9NE40AUmOPXBm9xqtBwKXQ+TjpoqO1k9nqyCsWpkwWDW+VHshOaTiX1YZXgz9enNXoWAtWPR4LHhU27K5iYt98ez8rVr+L2fbU/vl0TfOjIxVdu0QzK7NJdzw9sEDmC7iOfsckFSTLHjdH1Hk1eK/0e7ym4fl2fyWaUOicLEe1VgpXWhs3hmEwcNZnrNxRzi2vfIECnJXkx+cU+ByCgnQ/kwfIFz/GIWckMVpDgtZzQ4tlzRsBHRJ8aIogxqHgcSlMH1hAVRhChsnvhsHhkMlg7drEkJ0sk6isyY1FRT07pDu5qbG4NJVvvq9h/s296JEZR5zHwbybSihpF09RZhx3vr6ajgnyM3uaptK9bTypcR4CHo3Fo3rxzJAiJr29GU2Bm+euwmfW03EJhViPRudEPwG3kyWjz+Hx/vloqsIzQ4rITYslOyVAwO3E7dLIS4vlqUHd+OieC/n3nb3pltmq0VeloguLPbq0lHfu6M2Uawtk8pgieHRpKa/f3JOXPv2OrCQ/3xyoYct+mYg1c4hUxNPe+7pRSQXLgCT4PeSnxzFzaDHvjO2NogiCNaFGlIXlFH3lxh4EPE40TWFSv3xb2bscKrpuUFcvK1hapQ8mD+iKU5OUlFXGIDre3tovWglbiObso2vgRCdIRfswmla0hAaHbtMywlbWcTR+aITedCR/spKmzghFf7RPRlqK4Kd8T8olICVw7KQpC1YNmOpmjOru8iNXNvpwRHKAlDiXXdmyaaavpaSb+pMz4p1EzO0eFcoOHXnFqimTNS6uDctfmqLYTlSHkHWCspID3PDi5/axHRPddEySo+wNTT6vWBuRhnT0/NW24rZCMTftkdcx9vW1rN5VQTii8+oNPfjWzMZtGl767IdbMYC7ft1gZDomuemU5LaN3+a91VTVG43KK3RJ9tkj4+owjP1VJ6b2l5xxdT1s3l/BA5dlk5vqQ1Wlgdmyr5bKeoO2bTwowN9uOYeidvGs21VBuwQflfUGq3dWsPzrAwCktfKgCEG7hBiyU6Whevq9LeSl+lGEwppdFei6QYcEL7quM8bMHrYMqleDR/vmU11vcKCyjg17DqPrMr69bRsnqiKNya1zZfJQsLaeBTeXMGHxBh7tl89zQ4sY8fIXhOojTBlYYDtjdd2gS3KApwcV4tJU2rby8NjSUsYtWMOEK3NY+Ide9OrQipyUWNq38aJpCq+NKGHL/kpuemkFv3vqYy6f9gl/uTKHgMfJxKvzKN1XQbA2xH2XZDFjcJEZ1RNi/MJ1tmM2HNEJ1oQY/vxyuazrhEIRuxplz7Na88SAroRCEQbPWsaK7Qd58Ioc/nHbObw79nw0RUbjWEW9rJLC1hej7l64lmBNiMn9u+JyyKQtHYNfT/6AHQcrGTP/K8qrQzLk8qKzuGL6pwRrQrYzVdcNgjVyezisc9NLK+j16HsEa0LU1ke4e+Fa2xdRXh2yOXsrocqKl79o8oeUBWtsBV5eHWLEnC9sZ68lL2BH4FhRN5YSjs6eHTHnC5vucTtUu86OBetYOLpzNXp9eXXopHLzFs4Ijv7HxNGn+BX2VPxw1ctTjeOpY388sGq4N0Vz8elHg1dt3lhFI7OVix0H647bCIJMICqPEkIgQySj4/R/6NwxDsmjR8Ojyro66fEqOw8deWCXFC9b9lY3ivO3EsZ8mqQH3r/nIurCES598mOqQvoRs4UYrYFasyKUqsMN370d/NwyaiMyYaowPUBtOMzGvdW2bNHX2y0t0OhzihYCLpVIJIKmyT6yahQ19Vl0ywiwekfQTtzq3jYOj0MjWFPHavMTiJ0SfazeFUTXDRaN6sWEv69n1a4gfqdCXmocYUOndK+M9e+SFMDpVNEjMsxpzfaDGLpBpVkf6c7XV2MYUF4btr8EhQKhUIQV2w+zYGQJI19eSWUoTEn71pJbNuu5r95xiGBdJKodgzdGncPVM/6Doii8c0dvO6Lngcu68OspH/OvO3vz4JINrNtxCKEoMkHL/Dbub6Z8TEm7VujID8Kv211OZV2YbhlxzB3R01au18/+nE37KuiS6GfjPpl/0CnBxysjZLmL/RU1XDDpQ7q3jWdzWSVdEv1s2FtOZV2Ed8b2tvMN/nJlDn9avIFwVIXc9XvKyU2JJRzR+XLnYd4bdz73L1pv19KBhu/MAnY9Het307rx0bAMg/W94Obq4kS33bTuzonWu/k/xdFb4ZVWRcTjQbSSV5ElbaNxtDryxwuXAJ9Txa1IpRLd3NHCQJ2OI+kMCx0SXfz5sqxmtzU9Zs+hxry7NdrNaNVwXit2v2taABcNoZUdEjwsHtWLl24sabZtr0PYUTzbD9bZ7fhcCtkpMXZ0kZUTYH2NKeBS7fT+GK2h1ITXIY5Q8u2P8gEAn0OgCEmrZEXRWzUR2T87D0Xkl5qiYvgtJT/n+h6NHtb2iV5i3RrtE31UheGWl1dy13ypcOaPLEEVUskVZsSSm+ojJzWOUASyk334XZKTDrg1BFBZG6Y6LPty6Zhz8boc7DpUy4KRJXw2/tcsGFlCcUYsAZeK36XiVFViPRqF6QE758Krwss3nk1tBN4Y1YulY85FVRU0AQUZAQIulX/f2ZvFo3rhEirdM+MobhuH36kw9ZoCJg/oyo7DdcweVsy/x/XB63Lw9h3nUdw2jutmf86276vplhEgLy0ORRWypEFaHDkpsTw3vDvTry1EUQQT++ahKCqaQ2P2sGJunvslC2/pxb/uPJ+S9vG08bnQNIWpAwrwuh0sHXMuz3ywjX+MOZf/3HsRMwYXMXreKu5euJbJA7pSkBnP4lG9OKdTAllJfgzD4IHF6xFC8PofejYKZeyYGOCje/rQMTHAjEFF5KTGkZsmnaWzhhaT4HMT63XwUN9ctuyvYmK/PLpmxPHO2N54nY0TVpwOlXfu6M2LN57Nh3dfwLtjz8frdthRNgG3k55nteZps6b+izeezb/u7EP3tvH0n/kZo+Z9ia4bdiy/oggUVTB1YAH56XHMGFLEnJtK+PSPF5Lg9zQKx4ymUKIjayzKKTpCyEJ0tcxpA7uhaUqTHAbs7dFtR5dW+MVTNyAVx7zPt/+oYyMAum4rLZDx2T6HwKvKjNETRZ0B/++yLtTqcuRoPYZOIWu6e5pJpGoT52o2W9aJrBvzyFuyLowCjWSNRB3jFthZpx0TvWQle4gguelt+xtG+c8MLibgUrjnt1nUIUeqU/vns21/DVc/u4xJb5eSmxqDy6HiURsUc7uEGEr3yiQqtwLtzBr2qXFuNu6panDC6gYKMG7BKgTwwvDuzDGNR1VY9olLQMckX6PEKAR2bL9Ha/B1uIWkQTLbeKgJG3hVTSZVmdu3ldUxtX8+ew7XMfmabvanBLeXVdO9bTxt23gpzowjJ0UagV2Hanl+WDFezcGCkSW4XRqb9gURQqGVz8nZ7VvRKdnHtv1VrN9dyYQrc/nHbeeyp7yWsxJ8tkHITfVx3UuyJPG2AzJMduLVeVSFdFr55Av49Htf43U5mH9zT7KS/DwxsCtv3XYukwYU2J8x1DSF1uZ50+Nj6JDgJzc1jiW3nUuM00FuaiwJPjePLS1lxfZDKIpgyjUFaJrK/YvWE/A4mT28mLsWyrrviiJI8LnRhMLCUeeQlxZLjNPBU9d2Y8bgImYMLuLxfvk4HeaHuK1YcreTrhlxvDv2fC7KSebTP15Ix8QAiQEPT/Tvyr2L1tmfr5s1tJgOCX7CYZ37F61v9OUmiwqafm0h0977mrr6iG1cZg4p5t2x5/PIUvk8TxvYDZc5EreyVl0OFU1VmDGoyObNY71OPrjrAjomBnh/XB8yWvl4flh30uNj7Frz0KAsA56GD3ckBjyNyipb9Wqsj424HSqJAQ9zbizhg7su4K/De9iliGO9Tp4f3p3nh8l6/M8P627LZGW+Wm1Hy9Dc7+acq1a0TbQ/oGl70Tha280t/1w4I6ibY4VXRpcxaJfg5Fsznjy6rkpzH72O/nzd0XBWgoev99cw7r/aM/l/v7HX56T42LSnslmHYZdkH6V7K/G5VCJ6pFFZ4iwzzvvnxNT++Yx9fS2P983lnkXy87sLRpYw8pWVTOlfYNdlsfDw77N5/J3NlNdGbHmsj4Pnp/nZureiER2Rnexj494GrlJmBAtmDu3OkNkr7AiVN27pxaS3N7Nm56FGIZZWrRifS6WySdEgj9LwPYEYs9ZMdoqX3YfqKK+NHEFreFXIzYhnxbeyvLBFrVz/0heyqJmAYNQHqgF6ZMQx5doC7n1jHRN+n8OAmZ/RvrUXp6ryUN9c7lu4FqemgoDaUJit+ysJ1kYozIzly+3lzB5WzNgFX/HMoCKGzF7Bv+/szfhF6yjdV0FuSiyKKiiuRAwAACAASURBVLjvkiwGzvqMYF2EWJdsyzAToCpDUp68lFgURTBjiKQ+fj3lQ1nmAIHTpfLg5dk8sGg9IT2CU1GZOUyWOLDKFpz72Pt0TQ3gVFWeGtSNYG09v536MT07tGbi1Xnc+8Y6+WETgaw3s6echaPOYcDMz3j7jt6Mnb/arl1jUQHQkH16waT3eWfs+QQ8TpsiqKuPMHreKnT5HUCmDixg7N9Ws2FPkLzUWLu+fHSVyqZJTze/shLdzMKz9o8ud3A05XW0z/ZZ54mmP+xnM2ofK5TxaDTKsRBd5vhoFSiPV/FGh1n+2DZ+DP5PUTfHQnStGkvJA7iiYtuilbw92taPbcT2lcugO0vJW+OKnQdr6JYZ22idhe37K+mWESAnJYCmKMweVkyMBgXpfrbua1Dy1nFehzCrVza+4VailBXlktlaPiizBheyYGSJ/VWoB80Qv9S4BrrjsX9uolOCj5GvrrQ/rmKdb/zfN5IeL52vpXvl91otZasADq2xHFqTp6AeOauY+I4Mw6zVITvVx9XPLqO2PkxHM+rG65DH5phUy8SrZP2QTlHhlXU65Kf7eWvMueSlxtI13U/pnmoev1pGeuSn+/FoUvau6QFUVXD3bzo39J0Gjy0t5cXruoOQH5guzowlLzWWov/P3ZuHR1Fmb/+fql6TdHcCkoUs7JCQlbCIiIw4i6KDiAgIqKAgiIiCg7iOL8PPwQ0RwYD7wiIyiIgMozjjd0QRESEECBB2EUKAhCXpztLpper946mqVDcBcb76Xs7vXJeX6U519VPd4Tyn7nOf+9a+n32VXh7+oBRZM8tYevflbC+vYdORszy5erehXBYIhDl0uo73J1whpJQ1N6y3vj6CJEksWH+IeKeVRJeTF4bnk5Maz9wRBTx9cw6j3tgsyPmIZJaV4kGWZTpqVoKdEuOQrRIBLXHe/56Y1N12rJp9lT4euz6Ta1/cwJ4TNRw8VcuekzVMWLRVODcFwxoF8DfYrRa2HD3HvUuKGfrKJrpnJDDzJoEz67IET9+cI6aTZRmP00ZOWjxef5DdJ2rwNgQiJkvHL9raBDNIwl6wMRjm2SG5xrFFowqZO7yAPZqpysLbmhyfzLxyvVIFIhqS80d0o2iUgI7ME7nRk6nm0JNs9ORrNIVSp0ves6SYce9sibDs+/e0q5vVm2/uvS72+2hjFv3Yn9IkvVCS/08arf+/p1dG73vN3fg8cX1nMlNisAGTr25vPJ+ZEmMcP3toARYJ9CFafSKzuQholWmMRQzi6Jh8+1ax/HVwruEyZZ6MrQ9DWbmXsX3b4W1UGLu42NCMn3drk1TqX2/qissuUTSyOzEWeFn7o9XjxBlxK9BWkw0+eiaADUGnq/WHCGsNpNlD83HZhNtR52QnDuBQVR3TB2TSo00L3t10BBDTq6/dVijUD4eJ93IA6ZeJ6++cFItVS05x2mb42m2FxFptFGbEU5DmJtYKf7+/L3OH5bHneC0dkhwUpLmJs9nIbu3iQKUPBQHrvHtXb7546GokbUN96G87BPxytolb3z7JwY5yH0+sKiUQCnPohI8YK7z51ffIwISrOtAQgs4pMYTDYXwBlbHvbOajSX0oSHPTKcVN6fGz3PHGt4DEtGu7cKiqjqduzuH/DMymd7sWdE4S8MNjN2Tym+e/ZObqPWSlxOG2yzwyIJMtx6qZfm0X9lV66Zzo4mxtgNqgSn0gyOzP9gPw4rACnh+ax4eT+uD1B7hx/td4GxqZ9sFOHl5RSmaSm8//9BtWTOjNvM8PYrdaWHnvFTgtgg7ptFqZ8ttObDlWzWM3ZLKv0seHk66kW3o8744VOkIuh8yH913J2ilX0aFVHLsrzuEPhJi4uNi47X9+WB7rpl4ldHRaCUhjSJFg1Tw9JIcpy7YzqOgbpl/XhffuvhxPjJ2nb85h6CubBFPFH2TmoK5MX1nKXwdnG+wVgM+m9sPrD3DVM//i93PWc62mKe/QYI81k6+k7JTXEFIDUane9dZ3xoahJ369gq+pDxjyEzqEM3tonsFvj2aVmJOYzmH3NgSMCVKnzWKYbeuDUk6bhb8Ozmb3iRojKesbj/5zND1SD31Didae169l9tC8iIlY812GWfbY/HnoLB/9sfl10Um6uXOY1xb9+Jdi4fxXQDfw0yZV42wSSfE2w+T6P42fi0XzS0SX5Dj2n6qjIN3NjvLIwSgJYSjuNcEoZtcqHWrRj1URkImqiknRzOQY9p1qiIDG3Hbh8BSrK1TaJBqCKkvG9jJs9/SIsQkDluj3MkdBupv9Fb5mJYklMGAV8xpdNgl/SMVpBUkS06tFIwq4f/kOVKAgXUzB5qd52HnciwzkaO5hXZNjOVAlIKw6091f7/Yt8PoDlGmSwQoQ77QQDIWpD8HSsb24//3tqAh5hhtf3ojbaWXlvX14ctVuNh05S0Gqm50VPmREv2i1BjW9c2dPY6pWv47L27RAQWXLUWHq7XFYqA+GKUiLF3aGwKpJfRj66ibi7FbatYphR7mPwjQPh8+IHkLbVrHs1L7z3u1bYLdYCITClJ0Q7lKeGCtdkz0gQdlJH6oSRrZY6JLkoqyiGm9A1SwvPTi1JugbY3rSGAxz9ewvyEx2s7+yluwUD6Gwwtaj5+jZpgWL7u4dAeHMHppnKF7mpoomMSpGYxMwdHB0KEmffNWnRRuDYX4750tW39fHUMe8ft7XfDrlKkN5E8S0benxar6cfo1x3pmDupLojolgsURPouqUyKJRhTywfLsxgAUY62iO9QJETM0a8NcL61EVhbyMFswZlm+sVRdI099bf72iqOc5Y0HzU7H6+5kngX8s/itliqf0b8u89T/8r8+nJygJIT52sBmqYnREY8VPXN+ZWZ8eMPoDzY31R4fLYSGjpZPpf8hi7OLiiN7CpcolR29oTgtktnZz4ISPtolNWLqe4HNShR0eCJjjiT92Zc2Ok0JGORTCYrEw5oo2PPhBKW6HBVVRePuuyxn++mageS3+OCuktBQSyu1aOTlXF6ZoZDfm/Gs/hyp9tE90sbPciwq4NbkAgOzUOI6dbkBRVeqCKjEWaJMYw76TDQK6skmEwyqZafEcrvThNZkBF40o4I2vD/N9VR0dNY75vhNeZBlkWfwDUVWVeSMKmfq37UKzZmA2j67aTZxdonOKB4dswR8OMevmPAYVbWTN5L6kJcQy5p3v+L6qlgWjurNg/SHqAkF2lvsoSHMb0sROm0TX1vEcOOXlzTG9uHvRFryNChsevtpY4+OrdlPfGOSlkcII+8mBWQxZsBElrNAQhkV39eLlLw6yaGxvwwvWHwwx/bpMxi/easgSW2UZfyiELEks0KiMAP1nf8mH9/Zhzj8P8PSQHHz+EMNf2UjnlHi+r6oDSWLZ+Mu57c3veO327rR02fE4bTg0KG6i5kD21OCcCHqh/vv7l5bw8u2FeP1BZqzZg4zQonl05S7e0tRLy8/V8dTavTw5MAuP08a0D3YS0BqxOv4+6b1tPH5DJk+t3Wsk+6KRhRGDQzpGb05aZkwcmvD2Sm+D0XjVXwtEYPw19QEmv19C0chCQzv/+nlfs/GRa4yGqlnYTYd0zJuL3rSN7l2Y+xnmdTUnSaBX8GaDc7Pcsvmup7lz6tHcuX9JCYRfRaLXPWPtMgR+Rmq8Xpn+J/FjiTmntZvdJ0RVZZNEs7JNKztHTwfonOzkwKnzDUbibDJ1wZ92gTKigbrjuC/iDiNP003xOCz4Q+GIZJ2ZHMOJc414A0KPfveJuqbnqxtZfk8fbpi/0bBZdGpc8ejL1Tnkuk68x2EhM9mNoqqGb6xZJ6dr61hibTZKjtacV8Vnt3bxQ1UtdSGxST0zJJ/hr3xDXVDVNgwJSZJQFAVZbrob6dEmnueG5nO2NsCtr28Wxzos1DWGUbSfs5LdhqtSyfEaerdryRMDs8hOFZK7x87W8sd5G6gPKvRo04JQWKH4WI1hZpKX6uKHM/Usv6cPI17bhCxbCAaD1IWEDs6BU166piaAAsXl1aybehUzPt6DElYJKMJ1K6d1HBU1AdZMvtLQVR+/aCv+QMjQlXl+WB43LdgkbO40P9gFt3c3hMP0JKUfM3D+19T4w6yY0JvMFGFl2BgKM2X5dspOeOna2sPekz5yU+ONivWvg7MZvGAT/54mNii9en1yYBa/f3EDn/9J+LjqA0aTl5Wwq6KGNZOv5NGVuyg75WPpuF7c9uZ35KbGM2d4vsHGMSfmAfO+JjPJxTtjL4/4ns0Vv6KoETg/RDYt9aQ5eVkJiqKy8PbuxuZgrrT1iE6mld4GI6Gbk3y0xny0F6z5fDqP/UJ+sBeKn8p5j94A/hN/2Obiv6oZq/dM9SSvU+KjJ0v1uLjjKqS3FB/evlOiksxqfT7vvTmZeXMEVJj2u/YRz5nfd0yfNsbPOrHn6OkAfxmYycFmkrxYlxjgj7MKBUo92rcSzcsYi4AKdH/S3DQXsTaJJ/6YjUwkjPTD6XribJK45daSvD6HsP9UA2/e2QuPw8KRyiZlzX2nGqhtVJi+QvxRd06JZfmE3uRltBCyBNoHrnu5tk92IQELvzwIQMfEOGRJ4kClj7x0Nz3axLP07t5Gb2XviXqsskyONnnaITEGl8Zt1WeTY2TYUe4TtE1JSCHEWaE2oPLSrYXUBiHjshgK04XG/X39OzF04SaGa0keICvZzeKxvbBIkJXixm6ziIEYCb546DfMHJzNHW9todLbQKW3gYeW72TxuN588+hveXlUIU67lYJ0DyFJ3JHIkmBnPfX3MrqmxLPy3ivIah1PQbrYQOpDKntPenlhRD4rJ17BU2v3EgorlJ30Uh8Q38ruE3V0aBXHn1fvMZQSnx6SQ0l5DSFF0RrFFr6YdjWJ7hgCQTGsdN/SbQSCYR5cLpLUwytKWTflKhLdMeSmJdAtQ0gxA3j9QqjrqcE55KTG8+rtPVgz+UreGCPogvNHdCPRHcPf7uktplFNWi4epw2LBE+s3GUkHZ12+NnUfsIgRIKl43rx3Lr9ZKUIr9npK0sN60E9PDF21k25SjCAtNAbq3qSnz+im3EHAE04uo6HV3obuGbOl0x6bxuhsELp8XPGnY2upumISoLRFXOSJybivLquvtlCUMfydTgl+nw6VdOseXMhtcnmXvtTNoULSR380lOx8CtJ9NGhJ/yQBplEy/s2Nxxq5rWXn430Fm0Mnf+Ki30/EiL5vLnxKFkpccZgjQ6pxFoxaI7R8Ze1+y6ozaPfXdSF4NjZpj+m7zUDkoYwjH57C2Wa2uLhU3XUBlXuXrSFrCiNmoyWMedx9p/9dJ9xzXP/dYCOiXHnVentEx2UabLK+0/Wc+vrmwmFFSwWGYskGtEv/FN4ue6tECJcKIJdM2uIaLLVBRR2lvuQkZjzrwN01jbSWJvErCG5gqqHEFxr1ypGaNefqDP493OH5VF2sp5gWDEEyCRgynLR1NtdUYsqSdQGVMYuLiajpdNgBkmIRuDk90vIT/Pw5phezLwpm63HqtlytJoztQGGvbKJv93TmynLtvO7F75g89FzDF64Ca8/yJRl20EFp8XCuqn9yErxsKvCJxL4zTnsq/TxyAc7KT5Ww67jPmYNyWXd1H5kp8bz0PKdDH11E08OzMJutZBxmdMwoQewavrx18/7mpr6ABktXaybehWvj+6JgsqAlzYYxzqdVgozPMLFSVHZVVHNA8tK2HTkLF5/UHiW3lqAwyLumI5X1zN4gbiup9buBUTiH7xgE4erfEZT9K63vuO6uRsY88ZmA/MFkZx7tm3B/tO1hvk1YPDP54/oBhKMev1b/jo4W5i5aFz72UPzGPDSBqMJqWu5lJ3yRcgCm7Xh9dCbrGZj7sZgmEdX7eLj+/rw1p29eH5YHnXaP3qzE1VzjBzzc+a1LLxNKFjes6Q4QgTNfHxzksFm0TI9yZs58ReLH0vyF9Owia7uf+lk/6tI9LE2izHQIwOdksTtWKfkGJwmHrZOP2xO171Tsov0BFFz/2VgJi6bRHZqHLVBle+rAqTGWyJYPXoCtHL+HcKLw/KwWi20aRnD/pN1vDS8gM6madj6kGhedm0dZ4iE2REwRtGIAhpNa5QQ2DU0Ve7RomXmdTktYsI2zibTUTM0mTkoh7ITteSnN02MHj3TwLt39qR1QhOVMUVzqpIkCX8wxI5yL/5w5ODSkdON5KWLSU0H4HbIhBSFdpfFIssyfkUoK+amuchJcyMBR87U8+6dotJ7flge3TMS6J4Rj6KqPHZ9JntP1NMh0Ul9UOWRD3ayu6KOGIt4z6On64m1Qo82CeSkijuEzNYePE4r/rCAoLqmxOJyiOag2y7jcVp46A9djM9v7ohC3r1TYMhxNomQotAx0cXO416h6fJBKVd0aMnqSX147tN9hMJhAiGFvZU+Xrm9JxJi7gBg67FzTPl9J7YePYfDKmO3WeiWHs+stWXcsmAjHRPjsFst9MiIF9TGv5fxxMpdKGEVq02mR5sWJLqczBjUlSNVTb2RXm0TmDUkl9vf/I7V9/UhPtZOpbeB4a9+KwagkFg3tZ8BgTx9cw7fV9Wzr9KHLEt0SXLz/Zl6CjPieXLVbvq/sN7YlLpnJPDcuv387Z7edEryMHtoHqjw59V7mHdrATfM38jdWtX/1JAcumckYLXK/HVwNtDUVNSHiXThLXNy0TVrJFnGYW3Sc28MhiPEu/Sk5YmxG56s0JQodbrlgyuEVpAOv+gV9eRlJUxeVsKzQ3KZsUbQd3VtHR2nf3DFDkMk7ceSqXnK1LzhRIfOotHvKKJZM+bP4dMpV523Yf3UaK6Kby5+yWlYc/wqEr0/FDaMQhTgYKWofA+eaohgZRw9o1GZmoG5S4/XUl4dRAbatBSiVnsqmmCLippwhACZXg2HOJ/N8+AHpdT7w5RW1GKVYOziYvafimzo1odFlaqLhAWAYEBhsiaGpW9OXVJiUTWRFr1yP3o28o/LvC5FgdogBIMKB0/UYtXWowJTf9vFOO7JgV25/e0tTF7WpJSn9wXatYrlwCkvueluHLKYmtW1ZzIuc2KXZJIT7DQCKS0c7DjuY8dxHy1c4rZo7OJidh2vpfS4DxVIuyyG29/eQnWtn4c/KGXvSS97jtdQfKyGs7UBYq1wuMqPCjx5YzZ5qS5kSVxLQ6MiJAr6d6SsohaHDLPW7uXpwTkAlB2vpexkPaGgwvQBmaiqwpuje3KHxuRpm2jnsQ9KDWZPbVBlR4WP+mCA3FQ3J6r9bDlWzfir2vPM2r3sOHqWuoDKiFc30eGyWCYu2YoKzF63jyFFX1M0ohtz/3mAWJtMY0ih3h9k/ykfW45WEwwpHNCcrWRJQkYMSm0+eo6ykzXMvCkbVJi0dJtBZywaUUB9CMb0acuM1XvITBaqkrp4WGaym8ZQmF0V1Xj9Qe5ZUszBSi8PryglJz2e1+/owbyR3UCFzokunBYxjPXe3ZcjWyT2nvRitcg8MqALQ4q+5tjZWrEBAA/+vhP3v1/Cigm9iXXYmDGoK0Nf2cS+U178gRAD539NlS+yRxVtEAJNsEqSJ4YPJl7Bo6t2GS5RV8/+gipfA0/fnGNAJ94GIQzm9QearUQd2vm9DQEmLW1yhGoMioE3RVFxWCOlAHRjcX1tul+sOfRNRF+zXs03twbd5MQMx5g3nHuWFEeInpk/i+krSyPuBMy/j45oOqd5rZeawC80a/Bzxq+iGftrNB75tYfLKgS4ZPl8kbA4u0RdQL0gU8giYQiE6Q1JvaH8Y1GQ5mZ3hQ/N59sw79Aj+rE59OZuu1ZOjpxuvo8BUKCZihjX2owxih5xmoCaShMzKs4KNpuVjBZOSjU5BnNT2gn4aRJf0x/LgNtp4f0JV3DjyxtRVMht7aL0RC05reOItdkoPlrN2gf68tiHO8/T+3c5LHRNdhuUxVBYEU5LssTmw2dx2SUyW8ez7Wg1qgrPapPOuZqhh9sukZ0mjMllWea9uy9nxGubyEzxUFZRQ21QpXubeHYcq6Fbejw7jtcQZ7eQnRLP88PzjAbvvlNeFEWhNgh9OrRk3ohuwk926TbeuquXkVj0RuSkpduQZTGMNeClDXysNZQrvQ384cWvAJXaxjDrp1/Noyt3sftEDYqiUhcIc0WHy3jrzl4RjVqdwz/o5W/w+oNseuy3xjTuzEFdefyj3ciSdB4jxwxnjF+8ld3Ha1g3tZ/BqtEb3DpdsTEYpjEUNpqv0CRG5m0IRJh//HbOl4YmvZn9Yz6uObZNpbeBvs99wcZHrjlvAtdMibzUJm5z8b9pzv5XNWPbXfbT9dt/qUj4sU4v4kNLb+bAaHPw/0RYLVqErE0rOxYpcjrXYYG3x/amIQwZLSPN0uNsktHdjmtG5CfGCs/cnGM81rVugmpTg7pNy6aGh67LIwEepxWX0867GhXPQWRSt18kyYOQo3Za4Mhpf7Pib7G2JrzLo63dIsHbd4rGqzm6aOvu0tqD2yH6C/r1vjehDyvv7WMk+VgLxNpsxDut5Kd7CGrnagiL67Nol1uQEU+HVnG4nVa6ZyTgssvYbOKXx8762V/pI84uM3PNHnZV+AzdHf1SOiTGcqCqljnD8ykaVUgorLDvlI/nh+bRu0NLctJb8OKtBXRLjyc71c3Dq3ZTNKKAiupGVkzojdVq5eWRhXz18G/5bGo/Zq3di6oKu0SLRWbFhN4sG9+H9dOv5r0JffjioatZ+8BV7K308fCKUnZX1BgNUn9YzBXISExbsZOJS4v59vszRlPVXNXvOelFQVTZXVM9/Hn1HmrqA3hihG7Oh5OupGfbFnicQi/+7/f3paBNC9ZPvzoiydfUBxgw72sCmpn4Zw/2M5K8Dp38efUeikYWRgwqNQdzvDG6J+um9juvugaMqn7yshIGzPva8JvVh62ACONvh81iJHl9KEtPzEmemGbxc3PDt7kkrx93MeOS6LhQ1f7/Ar75VST6Y2cjb41+yuUaGu9Rj3XedkGa+5LOp7/eTFNsc5m1KfloEWuBvHQPNaaMpif9zpo0gC7yZY96bSdtAjYrJe6Cm4A/LHoTusFGojOGzx7sx+XtWlCY7kHW1mi3ysiAw0RNymkdR11QJSs1ntWT+jD/1qZJXPPn8tH2EzgtYohqr9aYlWhiOVXViOZ1nE2ic3IMDWH424TebHj4Gp69JZdX1h8WjBSrOEZfwYvaFGBemksYeFjEkFOnZNGQVVVxfXOH5WGzCBOQohEFuB0WYi3CCARAQaVjYhwS8P743uRntKBberyR7IXMsEju80cV8vm0/qyffjV5qfHkpbmZvW4/iS4nfTtdxooJvbHbrVhlmbUP9GXBqEKhQQ90S/dQ2CYeq1U0Ru+/phMl5V4GzhfuSbIsMW9EAT3axLPqvivJTUsgNz2Bl0cVcnn7lrx2R3eyU+NQEDj9s0OEbIInxo7XH6D4aDWdE104rBbmjeiGLElMXbad7eU1/HC6lh5tEujXJYl1U67i8g6t+EyrXnX99lBYoT6osv+kl6zWHuZ9fpDGoEii3oYAM9aUkeiOYf1D/Zk3qht5GQm8fFsheektWDO5L8fO+Zk7ooA3xvTk1dt70D0jIYKvDgJm+WxqPxaOEnDJW2N6RRh/PH1zDjP/Xsbek4Ig8NodPcho6WLOsHxDwEyP+FjBxnnnrsuZP6IbSZ4YcSehJXEz7m1OxBdioejNYD2RNgbDxsSt0yZMR74wySDU1AtWUmMwbMA/+iaiT+nqUM3FhMWi42JaOuZN4WIR3Rhu7jy/ZPxoopckKUOSpC8kSSqTJGm3JElTtOdbSpL0L0mSDmj/b6E9L0mSNF+SpIOSJO2UJOl8XlNUtGkZq9EgRZV2MbQqJurz0Pk0QUQDVH8cYwNVUdlX4bsk6z4dp4/sCYTIaOkwHsdawGKROXRK/MOLswpjjfJq8erx/ToAIFv05BeZzfXBrb0n6y7K0T94qsGwvSsur8FhlZGRiHXayM8Q1Eu7VcblkJlzazeDcbRfgyb2VdTw1NqyCLGzEKLB2xCC+/p3xB8Wk7K6zK7T0jQ5m5EYqw1XqQZT6LlP99IYCjNw/tfsrqjh4EkfdSGBZeumIu98cwSPw8LcWwuRUGgMi8bwIY3mKmvUmT9/VEpDUGD3k5fvIOMyJ/VhaOmyE2eFsopaDlUJxs/4xVvxNgTEtad7sEpizUfO1BtV5qOrduFx2lFUldLjPkqPn6Oq1s/CUd1ZsP6wkBKwSDz8QSk3zt/Irgohw+C0W7FbLGSluDlcWcf4JcUUpHtYNLYXO48LrXefP8Th0/XMXFPG3FsLkBE0Sass89AHO40+UEMIRr72reGGJBQprchIXPfSBqYs205IUdhWLmCX/DYteWpwDg8s3870laUGpj9+0VbGL9rKpKXbsFpl8tM95Ge05IVh+ew5KeQJAhqOrCdKHb4oGlkoXJosMh0S3Xyh2dqB4N9vP15jGHfoMMj4RVt5cPkOJi3dZlgBTl9ZakgnDHp5I49dn4msqXTqePeAlzZQ6W04T95Ax/d1vR2d9mhuluqN3+bUIvWEOH5xpMGH3zSAZX5s3jx0/Rv9vGZGjk4n1adYfw5M/P8FLfLnikup6EPANFVVuwJXAPdJkpQNPAr8j6qqnYH/0R4DXA901v6bALzyY2/w/Zk6VEBVz7/vT2sRtUQdW25mA9QboO0S7XRMdFEfBovlfD68OaIBmGh+/f3XNDVAUSG1hYPaINz7mw7UhYjgzCfE2ilId4vRchnaJ8ZGUC1jLTCmd5rx+FKgHYcEjSGFslNeZg7KRtVgmUBIwduocLLab/jUBhFwiiRJWCWJt0f3oHe7Frx2W6H2ucTQs00Ccz/fb5z/9dG9yE6NoyHc5HxVXlVPegsH9WGBd+emuZAlicZQGFVVKRpVaIjIZbRqUusMqypdUzzU+kPUBoWB+ZK7Bdtl+YTetG0ZY2x9L91agFsbmDh2uoE8TRitLgQrTKBlcgAAIABJREFU7+3DwttEdR8OKzSGwjx2fSYuh50F2ntnJXt4YXh+BOf65VGF5LSOo3Oyh+vmbsDrD/Dg7zux43gNT/wxC7vNwrtje9EtPZ46bcOz2yzMGpJLl2Q374/vjRWJ2ev2s3761RSNLOT2N7/jg4lXGJzwneVnAZgzPJ/X7+hJrzYJ5Kd7kIAuJg/TN8b05J8P/obX7xQ8dVmWeGFYPh6nheeG5TNzUDY3LfiGvw7OZv6Ibnj9AlIpGlXInOFC8O2JP2ZRcszLs7fk4nHaydIau2UnvTw5MAuHhiFfr8EXephZJ+Pe2cL4RVvxxIgkp2PlOoQzZ3i+kAiWmkbvzRx4RVV55tN9rJl8pZFUHTaLsCuMiZTs1eEZs4SwOaGbNwSzvoz5d/r6i0YWGpW8+XcO28Ubl+bNw9zs1SMaqrnQGn4sfiot0gwX/ZT4uTaTn9yMlSTpY6BI+6+/qqonJElqDaxXVTVTkqTXtJ/f147fpx93oXP+Es3Yn+LG9J+E7h7UXFxII8fCf2YM3ik5huNVDWSleijROPZLm9GY0UNG3N00KGLiNKxiyDUXpLmRZJntx2ouuLZofRq9adlD059x2SVCikpDqKmZC+IzkbSOaK322eSkxFF2so7cdLehz6KH7hKlS0zrjdzMFCGboB9jswqdHXNDdcWE3oxfvJWsJM1dSVWp9QfYcdxHXqoLp82KrEoUl5+jU3Is8TYHs4bmMmDuBjomN53/7dE9mPq37Xgbw8Z1exwW3hzTk+Gvb6YgXfivFo0q5M63N1NS7mXp2F7c914xi8b1ZtZaAWtIEvxjSj8eXbmLhbc3UQkBnh6Sw00LNrHint7csvAbJCQyk9zMGpqLx2njviXbKD5WTY82CSy4vTtTlm1nz4ka2rWKZcdxHysm9GbuPw+w52QN2Snx7DlRQygcJje1BU6nldlD83BYLVw9+wtyWgujj4mLi0GCPRU1ZKfGM29kN66b+xVr7u+Lx2nngeXbCQTDvDP2cqFiuXyHIa+s/6wf89TNOcxYvQdZlgxNnElLt7Hw9u4RsgL6pO2c4UL3/tkhuRFQ0V1vfYfVKlM0ShQe5g1g0nvbeHZILtNW7OTpITnMWFPG3OEFeP0BQwPHE2M3OPazh+bRGAqT6I6hUYOFzMNOZs0Z4+/N1jRwpbOHdD0dvSkbfaehs3XMUgnRsgfmY6Npmea1NRfR5zA/dylN2l+kGStJUjugENgMJOvJW/t/knZYGnDM9LJy7bnoc02QJGmrJElb9anJ6Or6p+x9bVrZDUVGAIt0CeVyM9EhycnzQ0SzUp8QbS6aS/JxVnGn0VySd0hNibS5Ri5AqkdrQGqP9f/rNNPd5U32dSkJTqNif+L6zuI9tG8zMyUWi0VCBroke+iijc8DHK6qw2GR6aBJCbvtMh2TnIRpmkjWk7zeXNYr+6cG5+JxWvBpjJ6cVBd5GQnkatW4qgpqqC5+mNU6FrvVgkOGGNlKu8vEe3ZIchBnk/ArQtJYT/IWi0T7Vk4jCYPYCN6683IK0z0c01QxY61w19ubqfGH2VdVy9NDclDCKj+cbaBoRAE/nKnHapGxWmVeGl7A3hP1bD56jkBIISfVzYGTDcad1rjFxSiKQkGamx5tE8hLc5GTFs+8zw/So00CLodN+Kv6A5RWeHHZZTF406gweOEmthytpj6gkNbCicNqYVdFNZOWbmOSNvGqoPLn1XuEf+yaMrKSPXRMjGPrsXM8sXIXf3jxS7aVV+NyWDhQVcv9S0soO+UjrIQ5XFWHBIx79zvKNNvA54fn0SEpjroQlJ3yRtjaqaqwEjxeXc+Wo2cJqSqSLBkN2rCiMKjoGxpDYWYPzWNfZS1VvgamrdjJ7hM1TFxaLAbMjpzF6w8IZyZJYtir37K7osbwmZ28rITdJ2qYtGxbhDLkxMXFlB6vZtoHO3l2SC7TV5YyfrGAh6p8DWw5es6QcdaHmsyKldM+2MmOY2cZVPQND13bmUlLt/HbOV/x0LWduX7e18Z7+/0hrp37Fde88CVj3txM/xfWM37RVo6dreWaOV8yeVmJoRqpw1P6OvWhqfGLt3Ld3K8IKQpVvgYGvLSBmYO6ivObOPa/nfOlOO8L66n0NjB+0Vau0QaqopP8NXO+ZNy74g5q/OKtjHtni7G25pQqm7sriH7u52rSXnJFL0mSC/gSmKWq6ipJkqpVVU0w/f6cqqotJEn6B/CMqqpfa8//D/CwqqrFzZ8ZMnML1MaBTxsKi9G0wGihrwvRBnUxM7Pq4qXEjx3/yLUdeU6bFr1Y6FWx2SDEHLr5x6WGaDqCEobklg5+ONNo/E6vspvzZ42zil6CoqhkagqEZRU+AohqtWNiHCXlXuEQ1bpJFE0Pp6ZQqVfQMRaxhp4dL2PK7zoZgmhOCex2mWBQoUGB9ol2vq8KGHc0nROdHKgSyVlX29TjUtVIOyQ5eP32y7ll4TfMHd6N+5YW41eaaJQfP9APh1Xm4Q+EDPLeEzX4w0JqeeaaPew9UUMgpCBLkJsuDE1iTXTNrNZx7D9RJ8TjUuMpOVbD6kl9mPv5QUPY6w8vfsmHk64kEFKY9Y+9PHtLLg+t2ElIUdlbUU1KC2Fes3RsLya/X8Lf7+/LwytLKTvhZeW9fXBYZYP6CCAjoaDyzNA8PE4bU5Zv55EBmVzmsvPwilKeH57Hn5bv4EBVLcFgiOxUUek3hsJ4nHYmv1+CPxDixVsLSHTHGNXfuHe3MP26Ltz59nesnHQliS6xsepV5h1vfMuLIwTzRYeL9MpZlx/4/Zz1vDmmF69+9b1BTWwMhamobmDOvw6ghFWj8ndYRX8gyRMToWhppkPq4m5vjOlJla+BRHdTY1OnPeq4uZ7sZ9zYlVtf28ynU64CMCp5h83CuHe2IFsk5gzLj6joAUPrxvz+FxI0098fMO5c5o3sxoB5X0cMgulV+vjFW3ljdJOpSnPqkpda0UerZDZX0UercF4oflZRM0mSbMBa4DNVVV/UnjMgmV8jdPNzxF8GZvIXzf7vUkNCaKvvP/mfialdikomCI/ccEhBls931gJh4rGz/HwD68J0D9Ouzbwg7GMOqwwmT2Xy090cPeOnuqEpReekxnGwos5oHktArF02Rtr18DjEkFZzYm/6Bt+cSxg0uVTpZ+yY5OBQZSMWoFubeEqO1uDW3L5qg4JW6JQtlJ2qoe1lsZRW1CID//xTPwIhhSdWlbL/pBdV1Xj02samQ0cuGywdL6SCV07sw5CFm1CAhBgrmcluFo3rTWMwzPenayNkiD95oC/PfLqPopGFTH6/hHp/kIOVtaiqiopKXUAXcJMJKwr1Ifjq4at5aPlONh89R4+MeLaX1/D3+/saCpXDX9+MxyGz6r6+DH9tMzmtPTw9JAeH1WKYcuuY/Ji3NrP1h3NIEnzx0NU8vmo3RaMKiY+1c+xsLb95/kth3L1aGNnoxh76pOzjN2Qy4KUNuBxW/qk5UQmzjxDF2gZ417tbDUNwRVUpO+kzEmM0DKE3fJEwVCcvBI1EC5aZk6bZDcu8mYxftPU8c5QLwSDmx9FwiNk5y5zEza/V1/NzsWOi5waaW2P0Z9Vc/GzQjSRJEvAWUKYneS3WAGO0n8cAH5ueH62xb64Aai6W5M2LuBQc6VIBmU5JscTZpAg451KjnSZV8P+t3Ud2qqtZEbULrSPOLkck+Z/KpX+gf1vjZ5ddJtYimqG6/g4I5lH7VnH4FeiQ5IloTEsISuMPp+tEZU8kU+lQVR33LtliKFeaTdVjNKjGgsDyddmB/HQP+elu3hzdi2Xjm9QKZcAqy0aSz0t1cXn7FnQxmYPrjKA2reI4cMovRMxMlEyAzFQBLWWlxlOY4cHjkHFp64q1SVgsYp26lMShSvGOOWkenFYrvdq14MP7riQvoyVuu4xdktlb6cXbqPDDmXpcNokebRN4ctVuhi/cSEm5VzCGLOI9AmHolhFPdlq8sabnPtlHj7YtmL1uP26nld7tW7Dy3j7sr6w1IITnPtkn5hYQm2Irl4OikSKxzhmWz6HTdaioSLLMkruvwBNjxeO08tKIQupCTRv663f2pE+Hlrw0shuXt2/JM5/sI7u1h9YJTiwSLL+nDzPXlLFuylXMGZ7PTUXfcN9SwSiZbzKzcdqtfPZgP7546GoNRqoxYACP084/HuiLx2kzjLLNxh7PDsnl6U/2sW5qPyPJexsC7Dnp5VBVHb3aJpCaEENuWjwOq0VYJt7W/TwZhOiQZVF960ybaAmC6Nfq/9c3H8CofHXnqUdX7cLbEGD3CW/E9Gxz7x/9nJnKaV6DvllG9w30tTanvRMd0VOyF2v0XkzrRl/jpWrSX0r8aEUvSdJVwAaglKai6nEETr8CaAMcBYapqnpW2xiKgAFAPXCXqqpbL/Ye0RV9m5YOjp5tPO+4DokxnDgTKYugV8AOCSwytE2KY+pvuzBtRQnJLRycOtdI7Y+YYACkJVg4Xh2mzWU2EmNj2Xuihsf/2JUnPi5r9viMlvYIYTIQjJesdDel5cKEIgR0THRwtKqRh6/vzJx1B4zGpS7JbAMy09w0BANGAuuQ5MAmW/jT7zK5570S4qxCTqBLqjDWAOjVJoG6UIh4ux1vYyO7NeOMvDQ3T/wxmzn/3M/ek17aJ8Zx4ITX6Cn0aJMAqkpZRQ0dU1zYZQthVA6dqqVLsht/OMwD13Rm4nsl5GsTqoVpHg5W+chKjufFkQVMfm8bO477KMyIZ/awfKYs30ZtIMg5X4jF43pz+xubMOvIWSyiSfvUYOF9u3RsL+oaQ9zzXgl5aS6syCiSitNqJRQW39C+kzU0BsFhE/LFYU3zXRiQiP9ntxaQxv3vlSBLEk8O6sqtr35DbmoLkMWofX0wxK7jPj6a1IdZa8vYdqwap0X0E3LSEgipCocq61h+zxWkJcSy76SXCUuKhY3grQVGwtPhg4lLinn1jh5MXFxMIBxm/ykfqqry9l2Xk5niifBUvevt73h+WB4PryzFKsvMGNSVQEhh1Ovf8twt+by98Qh2q8Vg2egTpR5n0z/wu9/dwosjCgzp4kR3DOXn6rj2xQ18+bBI6OaG4rQVO8Xfuiaw9ty6/cwemsd1L23QJKBlPpvaDyBCs12v6GeuKRPfgQp2u4UnB2aR6HJSVevn6U/2GVrvZongaD2X6IaneXJVhzKiq+roChfA2xA4zyrQXPUfO1trSEL/lErbLHJmbtheypou1lQ1T8lGn/9iTdWfun5z/Ffp0Se0yVQTRr344wf+BxGNBdstooL7pSLOKgZTG8KXjkNfDK6J1TB6vwoxVokGTXtANwzPN7FZzCYiPTLikSWhleILKMZaclrHcaSqzkj8EpCvGYaHlSYGTcQaTH0Al3Z99WFx56CqakRj2mXD2Fh/auhMJo/TitdEmSrQ9Pj1Xkqn5BgqqwP4GsP8bUJvRmg9gzjNBOWTB/rSyuXgdG0jN8zfiMthQdY+4Y5Jbg6e8uELKJoOfYMmF6DSKyOBA6dryUx288LwfAP6mLi4mFdH92DyshJKj1fz9/v7MnD+1/j8YeJM2vi927bAbrdQNLIQrz8QoT0fUsSUbDgUoiEklFl7pMdjtcrsO+mla2o8SkhlX5VmSC5L+AMhio9W06fjZcy8KZthr35LdoqHZ4fm0n/2lwbEk53iYe6IAsF/V1WKRhbSGAoz4KUNhoSAjrXroTsj6ayYKl8Dg4q+oUuSi33a5vXhpCu59bXNLBnXi5sWfMOnU64ykv11c79i/fRrDMVIiExwejMWMFhBZs13oFnoBIQOTSikUHZKwEIXMgwx4/sXStDRYYZFoqEh85r+k7jQhvdja/rfxH9Vos/K7ab6B87ivn5tWLDhaLPHOCVRhQU4PylaEXBD9D1AcxRLs6WeHs1V+jr1Lzr+MjCTj7afMCprc0zp35Y3NvwQ0Ry9ULK/UHLvkOjkcNWFdWD08GhWgUvH9mLcu1uMJq9umNJDU5cE2FvhNe6C4iwgWy34GsPEWCWUkHre56aHmSY6d1gei7/9gR3HvDhkMfwkqSrtk1yUapovsZrJd1hp+uy6pMRy4GS9YW4Sa5OpDyoR5442edGNTqyIKeTZwwt4ZGUpeyuqqdOUQzunuNl/0oeEaDx3SIzjiT9mM/ad78hM8XDodB2dW7nYVl5NdmsXVlnGbrVQHwgiy7Lx/cU7raiqircxTI+MeMHWscgEAmEOnK7l9Tt6MPz1zXz+p34kupwiid2Sy9T3t6OgYpctBBSFg6d85Ge04NlbhKb83kofH0y8oqlCRnDSFUXl3v4dGbu4mB5t4rHKTVQnq00mEAzz5p29qKr1M+yVbwmFw3w0uS+JLicTlxYjI7Hw9u6Mfec7rJKMbJV4anAOT3+yj8dvyCS9hYC3GoNhJr9fwpxhQlN+/ohuTNLgHqtV5smBWQZlUlFVAoEwdoeAcBxWC1Pe386rd/QwKvnHb8ikU5KHSm+DQeX8Ukv0OnxipjjqeLzXH+DRlbsijL7NmHxzJiU19QGj2atX/2ZzEB1y0u8qzJU+YOjYRCdWM1XS3Og0m6H8WPzYJvJLT7hGx3+V1s0PZwUb40JJHkSl2YhI3tER4vwkD6A2U7lHJ3loHs4JNfOkBaGCeOCE15D9Nce89T+cx4C5UHF7IVOV5pJ8XJSGjlNuwi3vfW9bBJNHtz188A9dKCn3sqPcGwF11YVhniaN0HCRJA+RNNEHPxDVWaxNNCyVkEJDSMUqScYfUX1IVOQdU1xGb+L46XrsiCTvFGLySKZzW4hM8hLw6nrBcAoBO8q93LLwGxRVQa9J6sOw47iPhrD4Oayo7Dju49bXN1MbFO5XHRPjsDss5Kd5KK2opaTcy7mGAKUVtewr94pJXYQk8+Jxlxv9BqtFZvp1XdhWXk3bljFMWFJMvNPKTE1SNxRUGDh/I8XHathxzIuiqhyurEWSJJHkPyil7JSXrGQ3HqegZsqyBKpI9MVHq1nwhTByeXJgNvtO+VAUlf2na3nij1lsP15DVa2fW1/bzMsjuyHLEkMXbmLikmLDj9XrD1B8rIYtR88RCikMf22zQUGs8gkK4APLtzPjxq7GlGtjKCzEyFCZPTSPGR/vYc9JL08PySGkKIaB+uAFQrd/7ykfE5cWM+yVTYZtoD6c5fUHyEkV/Yx7lhQz+X0hPaxj2GaVyEFF37DnhKCBOmyiqjfTHHVJYj3J+7UNStfuN+PVujb+gyt2UFMvICGzObeuWNlcmHXm9fXp7xc9vHWhiMbtL/S7X2P8Kir6lm2zVM/IOQY2n55gM2QF7EBGoqCvgTaUI19YzVAPM8xxsbjYEJMVsNskY9jIHBeyKXTKAo9u0E56KcwdM70zehCrMMPDjBtzDHaHfofQq43Al2fcmMPNCzehIiCNg6cayGkdxzt39ebepcWoYJhsd0x0cKiq0VCHlIE3R/egpcvObW98a6hg6rTKrq3jOHamgVBIwWG38q8//QaH1UJpeTW3v72FgjQ3nlgHt1+ewT3vlZCTKirn0nKvsBnUPGp1/1j9M8lNc/NDVR1hzXgEhDvWM5/spSGk8uG9fRj52iYawtA1JZZnhhQY1zh3WB4z1uzmzTG9GPXmZjJT4jh+LkBGSyc/nK6jY5ILqyR05nW9k63fn+HOd7YQY5NRJQkLIul2SBLety67hCzLrH3gKh5duYs9J720uyyGQ5W1rLqvLwDpLeIM8+y/398Xnz/EU2vLeHJgV0HjPOll7QNXMajoGzFFqrFidJu+xz/aTSikCEVLoPhoNb3aCN14WdskMlq6DNx5T0U1N768kfz0eOwWWVT+ktjgG0Nh+jzzb2JtMv/zUH8cVjHef7DSS3qLOMYv2srTQ3IYvGBTBDwz7t0tyLIkWEHaYJNeEVf5Goz3n7GmjJmDuuJxiilaXRr0rTt7GUNGiqpG0A31yj7a+k9PvNGqkcB5cEz066KrbP31+uyAfjcAnHfO5irr5ir3H8PeoyMa349e9085188R/1UVvW4lqDdgT1U31cEBMJI8QLvEOFSl+SRvrvZtFtkwM4kO801B9Fei/xnYEMk3rCW/aCHIo6dNzBrT+/gVsTHp8dd/NCX5aHVLPUKIStltl3hvQh9cNsm4Y3jsetHAkxACYEEgO9XFM0PzKD3uY9bfy3Bop42RLcgILZ37l5bgtFmZN6Kbca5DVY3kp7uJddhw2yVy0908uGI7s9aW0RhWDVaPVWOjlJ2oQ1VV/AosGFVoCG4t+Lc2U6AK0as3v/5evE6WsctCgyfOCm+P6WmwazpptoQAP1TWoigKVqtMXpqLWAs8+XEZ9UGVN+7owZx/HcCqfX/Hz/mZtbaMbukeclJdQptfhXat4vjioatpGRvDynuv4OjpehRF5VBlLVarTCgopBOqfA08sHw7y8b3plvblvx72tV8eN+V5KYnYJdkobGUEk9263gS3TG8dVcvPpvaD7vFgq9R4ZGVpQx4aQPehkCT1Z3VwnPr9qMoKgPnb8QfDJGV7KExpJDdWkzSTl9ZStGoQtY/1N8QAdNhIbtVpjDdg9Mhvh9ZlgwLwhlryjh2tpbn1u2nW3o8hytrxUYwNFfYEdoshltUfkYLA96oqQ9w62ubqfI1IMsSie4Y/q1p3eiCYHNvLWDOMNH4lWXJ2ACcNovR1Pzz6j2GKYiuK68fr1/7a3f0YM6wfCOhPbhih8H7Nssc6GJiZmaNrlVjToaT3tsWofmu69I0Vx2Hwopmuh6OsErUz3kx+CSa466v8VJDP/eFRNj0a7lQxd/cz/+v4leR6Bu0C9dpjO00lUc96cZaJSMh7z5R1yzXGiLx+OQEe7MYO0QOR+mHRLtWBRGYsw5tNEZ9Nw2mx9E3Fw/0F/o4RSMKeNokCfyXG7MjjuuU3OQOpYQAFWZ8vIu6oGpUusNf32z4pb624TAAeypqcVhlvnjoN8iSZDRQrRaZggwP2a1dyFZJS3ZNVbNTgsOnfDxyfSaybKG03EcwGMZqlVl2d29jgjYQDhMjiz8OXQX09re3MOYt0fR8akgOsRbYUeHjoeU7CWjf34ETXvae9NIx0UVdSFSQtdpGeehUrbHRtUkUk52KonBYM/qIs0tkpsQydnEx9/Rrj6oq+BUIBBW2HK2mpNzLbk12WFVV7l9awolqv5GAJFlGH4a+r39Hth47xx9e/Io/vvQVrePtTFhSzNM35+D1Bxm68BuxZkm87/QBmaCKylSvQGUkerZNwC7LrNNYKtNW7KRzogtPjNCDmTUklxibgJF2V1Rz3dwNBLROv6JVLw6bhYOVXh5dtYsZg4SnbyAY5vDpOmbc2BVPjJ03RvfkyYFZPLpqF4/fkMngBZsMOz9FVQiEhEiafs7GYBgUePaWXAMyAVgyrhcz1pQZzlLQlET7v7Ce373wBde++BWTl5Uwf0S3CJhDr6IVRTU0bPT1K4po8HobBPxR5WvgurlfGdOh0S5SgEHZNIc/GI6YPNXj2SG5hv2iHjq8E236oSiin9CoVfeNpsQeLbIW/d7RAmxw/uRpc0navB59jc3RI52mjTH6vcwbwI9BPL/ERvCrgG4uNDClwxQ/Nin7c4S5IRtjFWqEmclx7DtVd8mmHBeDgaIbjv9JxGkDRSqCiVJ0W3ceeK+EkuPnN4bzNDOLvGYGp+IdFhbc1p2739mCXxXHlh73ESNBg2mNnZKdHDzlJ9YqUR9ScdkkVt9/Fbcs/IYazaJrhcZ60T87C7BQa6a6HRaCgTD+qGln/fszwznmn6NDvxNQEd9NY6jpu1o6theTl23j9dE9uXvRFhRVxR9SyUvzMOvmPIa/8g21QZVYi6BTbj1aTZw20BUTNaBVkOrmyNl6QEJRwoYJSKdWLg5U+eioQT3rp1/N4x/tZuexc8bnoA9Z6QNFDy7fARL4G0MUl9cIfZxl2wiFwjSERBFhsVrITU1g+oAuDFm4ibUP9OWZT/YZA1H3Lythy5FzAMbv5gzP5/5lJWw+co7e7VtglWSeHSoExDYfPsOyu3sz8b0SumoCa3OG5+OJsbPz2DnGL95KlyQ3b2rzEf1nf8FnpsEogEAozJLxVxgV+uM3ZHLd3A38/f6+vPDPA0byvu6lDXw2tR8OqyViIlWHZhqDYfq/sJ7sFI9hdlJTH6D/C+tZ/1B/o2I3G4VE0z1nDurK4AWbIrTkf/Pcv6kLhsVAmGZgolNLdSaRDkcBzQ5A+YNhxr2zBatVPk84zWxsor9eZxbpHH59Y4ume+rRHPvGvI6LNX9/yrAU/JdBN3o0V1U7NSaHPsSkP5/q+fGlOy7yOzN8Y5MiG7Jh7R9/+VmhyuiI0pV3XmQI6kLwzMWSfLQSZ1aKy9Boh6ahI/OQjYKADXaYknxnTb9GRphfux0yNlOzVI92iXGMfnuLcSegI2F6ktchL12Z02IVEEfX1HhmrN5Dh0TB7ChId9OuVRzd2ojGnEWCvPR47n2vBAlonxhnvEesVTBcYq2gSNrdgslQRW+OStp5zOF2yHw0qQ/xTqErX5ARb2j+u5xWagNhJiwppnOSG5vVysqJfThypoEZq3eT2VqsrVEBVHEuSVVxOSzUa6bkLrvQ9t9RIZJ5Voobq9XKrJtzyUr2CHnfRoVDlbX0bNuCRHcMRSMLyUr2EO+04HZYsFgspCbEkJXsNgaKFFXl0Jl63HaJye9vR1GFEFysTcJqtdIlyU0gFObmhZuQZZi5eg+7Kmp4aPlOrntpg1iv00qPdi145pN9KIrKtBU7DRbPjBuzkS0C9lk4qjtrJvfl3vdK+Pi+PswdIbRepn2wkzFvirvCDokuDp4WxIfGUBgkYUqiD0b5gyG2HK2m/FydkbxnrinD5bBy25vfGcn80VW7WDP5SqavLOWB5duZOairoUWjh8NmIbu1B9n0ZTpsFjFwZRqO0ivgaOnghbd1J6Oly/DfNc6ZGs+6qf3IaOnijdE9jYS/QRNeAAAgAElEQVT86KpdEUn+niXFjFu0xdCo0d8PRPIuO+Vj9tC8CPEwXe9eZweNX7yVa15Yb1yXJ6Zp6Euv8C801Wp+HG1YfrHmr36HdKkN4kuNX0VF3zWvm5o58WW2HxNJa9rv2jPnf77n+SE5/PUfZYQVlfqgaiS5tpcJ3ZfBeUmsLq0EhIDWD1X1NISE1syjq3YTY4UOyS72VNRiUZsXG9OpfHF2mYzLnBw5VY9fEZxsGZVwWOX1Mb244+0tqDRV7RYJHh3QmXmfH6RtYiyxNhvP3JIHwE3zN0Q0VGNkkSyfuimHP31QSq7O31ZVagMKb4/uwdjFxcIYPSWWKm+Q127vzpx/7Wfz9+fwOCyEwmFeH920Do/DQk5aPE8NzmHaiu3sKBeDTYfP1rPorl6kJsRw3UsbyG7t4d6rO3D7203TsF8+fDUHTtYaFL+XRnRjwNyvaAyrhBQx4bqropYYTRpg7QN9mfXJXp4dksvjq3bjD4TYX1lLVpIbq000E6dd2xmX02oM19xctJEuKW5KjtbgtMDK+/qSliAgOa8/wJnaAIMXbqIg3cOR0/UoikJWiocXRxTgsFo4XdvIEx+JQaMFt3UnyRPDwUrBwOmU5GL7sRry0zy8O7Y3jSHBEf/z6j1Go25PRTWDijay7O7ejHt3C3+b2Ie0hFgmLi1mT0UNi8ZezjOf7kWWBD3xydW7CYQUYu1WikYVcry63qhg711SzI7jNfRo04LXR/eMaCw+MqALz3y6jznD8nFYLVwz50uD+w0YScLrDzLj4z2G5LDXH+DxVbsJabvs80PzSHTHUOVrMK5D15IxK0DqMgBPDsyiU5LnPKkAc2WtyxsAhs6MWRdm5qCuhlaOfh69KRs99ASRA1bN8dqjZRAu1Gi92NCQucqPtgDU7whyU+MjpA+aO6956CqaZw/nV9XNVefmSrw5TvyPXUv0Wn4Kp/5SqZr/VRX94dN1NAabMmPR/4jm3pLNR4UeSVCNGNX/4UwjTitGkgfRCPSHRKPy450nBQ0wBFZJJru1C4sM7VvZiZExmpcyTVQ+WZI4ViWS/PNDcrDKsqGFMv7dLYZBx9TftUcC5t1awKxPD1AbVNldUUdIUXjsg1Ke+HDXedf30shCwiGFP2km34cra1l4W3c0eJn6gLj2jklO9p+s59mbcxn15mYeuz4Llw06J7uoD0HbVrH8bUJvJGDVfVcyb0Q30lvEMf3aLEBU+Yvu6sW4RcV4/UHeu/tynr0ll/uXb8dlk3A7LBRmxNMYUhi/pJic1DjevvNyPE47+ektWHZ3b2It8MNZYRTy7rjeuBwyrVwOnrghiz+v3sNjN2Sy75SXBaMKsdpkUOGxGzIZ/dZmhr36rZgYDSn4AgqBcJicVBcNYbj11W+MhDxjTRmpCTHEyDBneAHP35KHLEtYrTIOqwWvP8istXs5crrBuIXxB8M8uWo3HVrFISPRPSMBp83K5GUlxjn15FhTH2DW2r3kpHoY9+53+AIKs9buBURC7ZLkZuyiYmbdnKuxZsp4fmgeLqfNSPI3LfiGx2/IBCDWYWPlxD68PEoMQh07W0tjMMxfB2dz+5vfGVh7fKz9vCQ/bcVOHly+g6GvCNaUxyka2jPWlDHt2s5YZZl5I7qR0dIlktLKXcweKgqGyctKmL6y1MCh9SalmJ61GcYejRprRufANwbDhDR+sJ7YM1q6cNosBsVRb7g2BsMRSS/R3cRLr6kP0BgMM23FTqat2EmltyECd9Z7GuYkrxubmPF2/XgzFdL8c/TGocNDDpuFT6dcZXDm42PtrH+ovyGVHI23m/F1/bo9MUIbXz9e/73ewI7G3M3r9zacL1Jmvp5oKqf5vWvqA+cleP256I3wQp/Hzxm/ioo+JrWzmjz6pfMokTp00THFzeFTPqNKjrPCa6N78X/+vovDJkZOviY/8NGkPox+6ztCoTD+8IVlDzolx+C22dhe7iVboxTqE6c5reOYcWOuodSYmdJksAHCdNnrbySsqpSfrkdRxKbg1pQRm/tUzb0FCchNdXG4qu48c2/hthVLrMVGcXmNcddRkObm+6o6vAGFz//Uj2Gvfkuny+IoO1lNbRDibGCzWnn3rl7c8somwqqgYc4cnMPwVzbSMcnNkTP1dElys6u8mgZFYNwvf36QPSerAQlfQKEww8O0P2Sy4ItDbPr+LHlpLkqP11KYEc/3p+uo1pxOCtM9WGUx2ekLKLw1ugdTl5egqio+0zXpW3SPNgnsP+Xl/Xv6UOsPMfz1zcZgWm6amzibld3Hz1EbFANfD/6hC6Pf3oInxsqy8b354/yNqNr5erRJQFFV7DYL+076WKENJ+0sP0d2ajx7jtdQ0xg2jNLfHt2Dt74+ws7ys8iSzBtjejJhyTaqG4KCgZTWgnkju9EYCvPbOV/xwT1X8Nwn+yg75aX9ZbFsL/ficVqp0Tr+8Q4L2a3jCYTDlFXUUJDRkldH9zDw3EAgzN5KH12SXMwaksuTH+3m+WF5PL5qN2+M6cnhKh83zN9Ij4x4nDYrzw7N5caXN+JtCNGjbQIHK2vJau3hkQGZzP38oFGdexsC9Hnm36iqMDL/cNKVPLl6N5sOn8XjFKyrrqnxlJ3w0rW1h93HqynIaGkYiQBGtQ9oksNeQ0ZZUYSZ+dNDcrip6Bu6pnpA+b/UvXd4VWXa9v1ba9fsmgTSE3pLJyQxRAbbM8+MOg4iQ68KAoKAYneUl4fXMioiUkQRRVFERFREH0cdXzsi0nsXpSRAKCk7bZe1vj/utVZWNqE4Mx7feB2Hh5u9V8/e133f53Ve5wlhRWHP8WpyUmORLaLQv1uzF5RkyahLrPvpNIVt49h3IkBWsg+rVWZm/1yDatot2YuMxO4TNXx8x++M7tm5g7szZfkWwhGFXWVVdEvxGc1kj/fL5ppZXxverSerhaxwdpqf+UMKRMOVqqJojvezB+dz7ZxvyUzyggw7j1UhIQkoSatbTF2+lV3l1SDBl/dcZTzbXk9+QY+0WHafqKIupFLSLp4Fw3s007c3d+5+bIKL9BXVH5/9BlVVyU2LNZ772Nc2sP3IWXIz4gxaqo7F638D8/O4VJOS31RnrJ7oHbTc+AQty/HqvHA9dMOK/FQvP54KUBPUKIOSZCTTrFQB5Zz3WmRIjLfx86kQBWk+gqrCrrJAs8SdmeLioeuyDQVIcxdtQYZfaMkcqzbw6Qvdl16ENBcjOyQ6+PFkI24btGklYKFNRzTDj7BKvSKUEqev2mlIHIDAwTsneZk1qDvHKxsYvng9HptE+0Q3248FkBA+qQdOBqgJKs2u2yoJmqhskemY4BYGHlqCBzGA5bWJY0afLG6av4aaoILbAlabhWAwgiwLmCeiCsxb73Pw2kXX6k+n62gTH2MoSRa2iWXPiRrato5hx7EA+WmC9rn9yFnaJ3o4UF5DvYLxPgrsKDtLTVAVpteSxOYjVXx2l5Apnr56N1Ou6cioxT/wzsTLeXDldvZXBOic4KFRUXBbraIx6HAlXruMxSLz6i3FjFq8ng4Jbg6erCEzSUgPTOuTSVqsi6ue/pJOrd3sPSG04N8c1xOv00pjWMHntDH5jc1sP3aWOq1bt6BdK16+udiAV+6/tgtDXvwekJAk6Nzaw76KGv5x15XcvWIbdcEQdtnCnpM1ZKX4UBSVR27KZsbq3TzeL5t73t7GukNnKW4Xh9Mq2C8LhvegItBA/+e/p2NrFy6HjVkD85iwdKMYMP4iZvUgcPirn/6aL+65ggRvTLNCqd65CgJK0+UPrLKgW1ll2VCJrK4PcsebW9h9vJpPp4peiknLNqOgMqNPFgkeZ7MZvS65oId+rsZwxOjSBc5Loayoqeev7+7k8X7ZPLxql1Fs1bnzLckR62You45X8+U9VxnHMnfuAkx6c7PRF6DDbfo16kVh3ef2ib/kNJOBjpY5joaFzCsh/dzm2fykNzcbonfQHAIyQ0Rw6Vz83xR0o/Po9WTYJr55+2vnRGeLjAxzkgcMw4qtZTXGMWvDmiGDFoHGlpD6pqhX4OdTIWJsEgdO1rCnPNCMj2+TYHd5HSNMMr8NikhuhRl+fqyoZd9xkeR17n0jotHHG11tRiR3Byb2iU3iR03crDYEu4/XsfFIFRLCgEPvcg2GFfYer242ANWFBdXv9898w/jXxPVFFJWdWrJWERTMrBQ/bptEUEFrcpJ4cUQhjQqoisJPp+spSPfhsgm+vd9pJSvVz4KhPfA5bahad2ttBFJjY8Q1qVDQJhavXTaMLrJT3VgsMj9WBEjXkjxAVpoXu81Cm3gnhyvEKunQqTruv7YrgZDKT6eahOt+rAjwVP9crDaZTkleJOBQRS0HTjS5Vf313Z389fqu/OX5tSiKyrR3d7LvZA3zhxRgt1mwIbHnZA1h7XvQJdlH10Qvsz7dz5vjSjh0qhaQ2HW8kvWHzzB44VoawxG+vOcqnhveg+y0WLol+xj64vecDgR55MM9mqVfFY1hUTxviIiuWf0HO3dwd6FCmeqnW5KXlRNKOXCqFkl7NoqiYrdYmDO0O1/ecxWzB+Vjt1lI8DgFx91pZ96QAkraxfHSyCJmDcxjZ3kVk5ZtxmGVefWWIn48XUc4LPoFDp2uZ8aNWfz1vZ389zNfcceyLSR4Y/jugavJiPc0S3a6kuSU5VuYsnwLPqedLgke0WQ2vAcLhvZg4YhCI8nr3bXZaX4Dopo/tIAn+uUwaOE647hOjePvtInagu7pqjNIEn0xhmdsNKNEL2DqfruKohq6+fpxG0wDVKIvppnipd9l5+Vbig1Gj8Mm2ECAweX3uwSVddGoIhaNLBLm5dp2uq+t/p7VIhtJXj+HudCqbxttpdig/f11VVDzM1+k1XfMz8t8/Wae/r87/iMSvdtuNXxNAc4EmoMt+082NGOmdNJ49uaQEV2oADmpXnLSYslPF85HZgrd4dMi0est+jFWqdm59ZAUFSSJN8eW0DWtyaFJRyTcdpncNK/hexoJKzxyUw5ZmuSuyyYZ3PvHbszk7re3M+1Pmc3OkamZoZuHHptF3OjCYQX4tJFCsGhEk1B+etO11IVVlt5aIjo7tfsGIbmbkx6HxyYGkC4pTc+rIRTmwT91oyGsUpDhJ97t5OM7f8eS7w+Tl+ajIQKdNFaNjES3ZD/vTCzFaReD7x3LtxAIQ/cMHyvGleBz2PDYZbql+lkwrAdZKX46JIr9d5bV8uKIQkBiT3mAwjaxrBhXwrGzDUy4sgM7y2tpl+DFbYWOCW66pfj57K7e/L+7r2DVxFIKMvxYLBZ8TiH7e+hUnWicSvPTJcmL1y7hsMrsLK/G57RxWft4ctJjeaRfNqoKwxevp64xxMajVbxx62X4Yux8dldvFt9cjNUqjFme/HgfH0zuRXaqMOxeeVspFouFqcu30hgW+LTVIvNYvxwUFfouWEtdY4j7VmwHJLpn+Fk0shCv04KCarArHDbBvJl2Qyb7T9WS4HHy9oSefDC5lyiuyrCrvIq7V2wzzL11eEaHMe5esQ279qP3xdjJTvXz4PVduWrmV/RbsJZXbi5CliUeWLmD928v5ZEP92h9GxK7TwgJXx1W0KUG9CSiSwrorJV9FQExw9USX3V9kLFLNnDtnG+ZdkM39p+q5Yl+OUYym7J8Cw+v2mVg6GaO+Mnqeq6d8y3hiGKsHqKxcPFdbGK76LDIlOVbRAfw8B7GoGBOsGb53ujVgJ4w9dctcdr1RGpOstGSwE6tESs6ohulznc/F2LUnC8uhV//r8R/BHQT37abmjpiluEz2lGTPNAhDKcm6aurM+ameQmrKvvLAkaSzEp1c+x0A1WNEVaMK2He5weZNTCPssp6Rr78A9VRHU86Vz4nzYNDsrDxaJOHqscmMWtgd25/cwtOi4okNXmkghDq+j/XZ/PcFwcJRxQD7/Y7LTw3tAfDF69vBtfoHP0emueqHvlpXhRUrLLMnmPVZKXHcv+1XRn04jouaxvHY3/J4Y7lm0FV2Vley0dTejF44ffUNEZY99drOBVoJCs1lh9+PMUti9fRKVnIGOeneVk0qphTgUYGvfAddSEVu9y0avA7rXRKcDPtz1m08tjJiPdwsrqeO5ZtQZFUgqEIm49Wk53iZnd5LcXt4nh6YJ7BCrlu9tdkpvjZXxHQNFwU6kIKH98p6gav3FxEP02yobhNHHtOVtMp0cMjfXNo7XEw9a2tzLgxi77zv+WF4UUGBLZqYimzPt0PiMLyjqOVvDmuJ1mpsTSEIox6aR17TlTz5rieDHnxe2qDET6+s7cBHVTVBbnt9Y3IknBwUhSVB6/vRt8Fa/nmviubuzG9sp7ZGsNHT+izBuYZUsH3rdhu6LbrwmCP9s2iMawYAmM7jp7FYrHw6dQruGP5FvYcr+GTO3sb7BRdG+b1McV4nVaufvprPA4LX917NaBhtIPyDXclXeHRzOGubggaM9vbXttoCJ8pqorTJmSd95yoMcxA5g8tMMTMzBK/ZuOMllghuoNStCgZYBRDo2EKaD7TNStAms+n79sSNzxaYOx80MX5aIwXcmC6GDvmfKFfw8WO39K1/ZJztXRPv3Q2/5vC6PWGKV3RsKWGKN39p12Cg58qWka8df0ZtxU6JYmijyxJVAYbOXjyXLGw8zVedUqM4cBJ4T/6wMqtxgAUHXags8mwWtdhSfLb2a+dzw4Cv1YwaJSuKP0cw3ov0UnZmQZqw6IBp2OikOfVY8W4Eka/8gORiMo7k3rx53lrWHJLk0l4frqX/cdrqAuLQunBigAZ8TGaRG49DuDl0cUkxzrpM+cbo+bx2V29eeidHewsO8sLI4qZ/ek+Nh6tEiqRKTr9MUK3ZMFJ33C4ku7pPqb3yaZ9aw8VgQYCDWHsVplp7+3AabPSEAojSxIz+mYz4/1d7CyrJBASNZMuyT72lldTFxGDY/sEN5GIwk+naumS5OPZId05HQgy5IW12Gwy/3tHb6OBaMfRs2SlxiJLEuOv7MDdb29l9eRexiB0w9xv6Zbo4+nBotX/4VW7mPr7TnRvE8+usko6JHipqKnn+me/pluSkHLeW1FDx9Zunh9RyN0rtjG9Tyb9n19L1yQv84YW4LBauO31jSgRFatVZsHwHkxcuokGjSn27JDuNIYVHFa52WCiu1Dd/Mp6MpN8TP1DZ7qa/HsnLdvczC1KLyjqRd0xr6xn+7GzdE708uSAPAYuXMf7t5cCAq6aNTCvGQVTd1yKpgTqmjDhsMLswfnGgGDGkI+cCeCwWgwcPJrW2VJyP58bU/Rrc7NRS4nTjO2fj5oZnbRbuhYzPz+68ehSk2hLg9aF9o0eqFo6XkvJ/FIGqkuJ31Si92d0VeOG/Tp69L9W/FL/138m9MFND6fUpBdfkOFnX3kVkUjTe91S3OwtrzWYKee7vMI2fjaaVhY6b76lb4JTho5JbnaV1yJL4HVYyWjlNIq0K8aVMEiTaDCOl+bl4PEaLBbBeMnV2FBWzu1lyE318NOpWmpMHWVeTVdeD59W24hEFIN55bFJBEIqPoeASDKTfOw6XoWiKEiSJEw2JJmOiR4Onarlyb/kMf6NzdoAWEt1YwQZIbzWIdHNliPVFGhF3oOnaumY4OHAyRpyUmOF5+7xamqDEWKs8Pm91zD5jc2s+1l0repdv167RPe2rZg1QLA6dpZXUV0fxm0XHQw1QYUV40oYv3QTHeJdyBaJTYcruaxtvEjyqsreE9XkZ8TzeL9s7lu5nbU/ntGeicTrt/Zk5if72H7kDJmpsYbQmSxJzB9awMSlm1gwvAdTlm/h0b5ZxmCgSyvog9e+EwG6JXmx2y1GoVNn8nyqsblUVSU7VRSnZUkyYCWdsTNp2WZ2lFUZ3bFmGqI5wUbb9LW0qjhZXU/p3z6nZ8dWvDxKdO3qsg56QVhntOirH91sBWDMK+vZWV5FdpqQfdYZK+aVSfSq4WIwSrTEwfkGqAt1sUbv29K//1U8/jdVjA1FhD65jpvrvXT281xdS48mPdba4jb/hJPgOREtaAYtJ/l/x7nMoa9g9DCbgozp1Y7aMFhMesdlZ+qN7uIsDa+PsQhzcz2Wji7mkb45zaz8dpYFDC15c6T4JRoUoS/kssE7E0r5YEov7FodoVNiDLM+2Wck+Yw48WXffqxGyAerKi4b/Hiyls7JrmZJvluyB49NYnuZYEdJYPQqZLSOwRyqqhoSxZkpLiSgTYILtxWWj+/Jp1OvYM7Q7to1eQGJQAgCQYXNR6sJhiJMXCYkGR7rl0tnzeowO81L50QPFoS14daj1Ww8UkXb+BisskS3JGHysfdkDe1bu8jP8FMXgrLKepBE01znRKdhwdglycfjN2UbXq6v3lKM2y7RLcVPp0RxzluXrCfVb2fj0SqUiEqPjFim9clkz4lqdpdXIUkyD17flT7z1jDthkwkxEoQ4JZXN9AQDFMbErBUMBwhGBQwTmM4wq7j1TSGIzQ0hOkz/zu2HjnD1LcEQ2X6ql101gquqydfbtAeQTRCfTK1N36XDYdVZvWky/nq3qtZMKwH84cUMLN/ruhX0GbR1Q1B5g8toEuCh6krtnLlU58zZsl6Izk/0S+HqSu2crK63hh0QKxgxryyvllXaUNIrBwuax9vFESdWp1iZv9crpvzLY2hiJHcZ/TJNFY/esgWiWyNLBBdh9Dx9+hkeyEsPHrFcCGVyuiagfm45jpBS8c63wrg14j/iEQf0bpW9Umd/ic0i4WZvVdbehRHK5vwlYw4i7FNhOaJ7lKjc6LTeDjRgmbQsgyCfgW/0Ca2WUTvez6Y6pU1P+GxCYhKj5Q4p0GXfLRvDnnpXuoj8OSnB41txi9Zz+CFa8+ZgVutVnJSRTLqqKlvllc1bVUbglGL1/PAyh047VYyU1wcOFlPWFForw1GR86KVJ6d6iY3zUNDSMgr1AQVjpxq6kHwOa0crggYlNeOCQ68DovB1vm5omlbgIzWLlRVzOb3H68jxgq7yoRL1tBF60Qbf4NQRTpYEaBrspfCDD/eGCseu0RdBLpnxPL6mMsYumidIQ62v6yGLUer2He8Gl+MlfwMAU3tOFbDrmOV7D6uyzt72HqsBkkRxuP9X/iecEQhO9XH/pMN1IVUvA4LTw7I42HNePvxftncvPgHaoIqsiThtFvJT/PSPsHD7vJaclM92O0W9lfUMnjhWjokuKkPq3RK9PDYh3tQAbtVRpbg+S8PYrFYeOPWy7BbLfTIED0Em45UsuFIJUpExWEV8gIA+04FePu2nmSnxiLLktCiL6ti74lqHr8pmwRvDEhCpE3XkE+Pc7N60uX8ed4a+sxbQ0WggSnLtzBp2WYma85a4nuVxTWzvuZYZZ1RoM1Oi2X2wHxjhn3vyu1GYg6GI9w4/zuq64MoqOzSuPfmQmljKIKMxL0rtxtNRROXbsIXY+fvd/wOh03YJYbDCn99d6dhNq4nykUji3hZ0++Jlg6ITqotJe7oMA9E5jAXnFsSNxv/+sYWfWWji8Hni1+zIPsfkej1OJ+Rtp2mQcDK+S/a47DglOHI2aYHFWOBp7REdx4ZmnPCaYEDJxuazYr1kBCzSssFVlyZKSJhmu/HfOro1w7TG1mpnmbnA1EwlhG6J3oIP1T4W79co7Hs2GlRF/DYJDxOK4dP19M9w4dM06qkNiJUI/UZvEUCu0Xokky7QShtHqyox2UTn3vtEjmpPq2Jy8uC4T2YPTCf1h4XBRkCT6+obBqMJGBPeS1WZFw2SSz9Ecwnl00yNPAbFaGK6ZQETbZjgpvMFD8S0CGxyVwcoOxsI5kpsRS3ieXFEYXYrFZD/2fZ2BIe75fNwIXr6JLoITPFj91mweWw8eHkXuRnxAsnJ0li1OIfUBRF07yx0KhC93Q/ORlxfDi5F6/echkl7eLo0SYW2WIBJB54dwcuu5VVE0txOqx4nHZWT7qcg6dqkUzDZddkL498uIeZ/XNZNKqIjHgPL2qNMXf/oQuKorKjrIadx6rJz/BztLKRp/rn8sHky5FlGZfDxjsTSnHZrcwenE92qh+f00ZR2zisFpnMFB+PfbiH3cerkS0CXipqG8fX913JK2MuM+h7vhg7OWl+EjxO7DYLs7TBp3OiB1m2iEQJBs3QbP/nc9rJTY/ljbElDFq4jpn9c5k1MI+9J2rITPHhsFk0+eMrePrT/Xx8x+/EoAFGo48eDquYlb9yy2V8cc9VJPpieHlUE/1Rn7nrngH6CmPqiq1U1NSz7qfTVNSIIm5jKMLCEYW8fEsxi0YVGVx0MxsHaEaTvFBcLNmaj2NO7Pq5WtKi0Zk60Y1OlzKw/DPb/tL4j0j0Fknor9u1TKwnvg6JYqZoswq8+n9u6EoYSDfx7J1yE62yTSsxo42RRTFz4bACVt7eC6cmexBSxWd6IpWBLskxzRKrXRKNP92SXfxUIcw5OiS6DS69BTh6usFg4FgRzTI5aR7y0wXd0qJpGwRV6KxJEbtsEh7t/nQ0z20VbCGrCX5xWC00ROCh6zqTk+bRtrPx4ZRevDyyEI9NCHNNvzEbt10iaJIhBpWuSTFkpviZ/v4uMpN9zBtSQG6aF00Hi8UjC1k6tpTGCGSluHlnQil2q4VgOEK8x05umpeFwwqwSBI9Mvy0a+3GIUv07BDPvCGChnrvyu1Mvrojh07V0RhWAMkYgF8YVkD3dD97yqupDal0TfZRkOFn6ehiLBLkZ/hIiXXSIyMWSQKr9gwe65fLQzdkCnORQd0pbONn8chC/E4Ly8aWICMRVlVGv7aRtq1dBCMChuqQ4OXhVbtYMb4Ep0OIns3ok8X8oQVkxHu44786ceBkAEURs2VVhW1l1bRr7cLrkJn25yxkSeK+FdupCDQgI/HSzcV8OKUXuemxLBjag+l9Mpn92QFkSWJ6n0w6JHhFInY5mD84H69d5v/emM3M/rnNEl7XZB/F7eJo19qNVZb5YHIvvrj3SmYOyOOVmyhtQxQAACAASURBVIt4eNUufE47n069gtkD8xn96gaCmoWeLEncsWyL4VD1VP9cZg/OFyqSA/L4dOoVLBlTcg7XGzA6LxeOKCTRF8PM/rm4nDY+ndqb+UMLmm2rJ9wn+uXg0AaGv320l7fGl+CLseOLEcn/heEafPTyDzisFsMkHERvhi4QpsMuOkffXLB12iwtSgroSVLnpSd4Yyjp0Aqf0250jpqvWU+yOi0zetD4paHP1PXEHU23jIZ/zK+j78MM+5jfN5/rQvFrJHn4DynGxrXppvqHzsJtk86RA4DmnacWBBzTvrWTn081tFhwNEviuqKkaKG5o5MedrkJKrJq55AAl9blaT5m1yQ3ZWfrmhUQQRhnS7J0zvt6dEt2s+d4bdP29nOdsvLTvOw/WUNdqIlmunhkIXe+tcWgiHpsokBWHVSamYMbx7VJ5KbFEYxEQIVNR6t4a1wJty5ZjyQJ3fZkv5M9xwN4bRIdkrwteuBmJrvYrck+fDSll2G20RAMGx2mHRLdbNXOr0tYmIvG3TN8bD1SbdyrCsTG2OjY2sWmI1XIkoDuzLFCu9bqRgWPTSI7JZaNR8/islnonOTFioQiqRyoqBWt929tNZLN7a9vYvOxSorbxTPthkyun7tGTCLsVhRFITPFT1hR2HKkCpdNyBUXZPjZfKRKFLElhAyxNvN9vF82Vz/9Fasn9dIokl/x3QPX4LBaOHQqYDh/WWWJwnThGPXyLQJG0KV2H1i5gx1lVaI7NslrSA8Xt4nF6bAaUrtHz9bS//m1fHXv1TSGI/z3rK+oaoxQkO7j0Ok6VEVQfasbwpR2aGUYgkcXH83yvuYCqv5aUVSjSKuLh1319JdkpfhAhZ3l1aiqQp6pXd9ps3DkTIDeT32Fzy5jsVoMKmlLFEmzeFo0Pm5mwpzPmFunZD7RL4dr53zLF5qwGTTpzOiCZ+YC7y9lsuiQiw7n6RLF/8rxLla8/XfO2n9TxdiGsGBA6A9bj9kDcoWXp0Vi/uB8LAj4JcYCh0410CXZ09LhyEv3UtI+jqWji4Wrj+mzdq0dzZJ8VooXC+CwSLhsEl2TYggjGqKK28WJhiSbRKaGXz92YyYnqht567bL8dgkslI9eO3CUclitfL88CKjiQrEAxYzZLhPEx/To2OCm65JMfg0SCY7VTBEbLKFjgkxQtQLGPPaRoKhiAnyUVl+WymlHeJ5bmgPPNr5irWGpPw28Uzrk8nGI1VsOiq6au1Wmc6JXrole3n1lmL2nwgwe0AukixhtwgZYq+G7/TQZtMum7CCyU3zkhbrMrw9nxveg9gYG6/fWiI079M8LB1dzPd//T2f3dWbdycL+z2vXeanCtHk1CnJgywJDv/KCT1x2q307BDPm2NLsMqiycvvtLBiXAntWrvpmuTDa5fJTovl6cF5rJ7Ui65JXh7pm82+UwGeGZRvSAJbLU3G0wfP1LF6Ui/sVgutPQ6K2sZisUisnFBKfpt4XhxZxBtjS/ny3ivJSY/FF2PlyQF5+GJsFLWNpahtHD6nXZhtDC0gwRtDbqqP4S+vx2G1UNKhlZAAeHMzo1/dwOKRhfgcooBpdzRfss8emC8GDBly0vx8OvUKXhpZxDf3Xck3913JSzcXM2tAnnHtM1bvNjpnE30x/OPuKylpH4fLbuODyb3IaxPHB1N6UdqpKckrqmoIdunSAOZGJj0aw4LhoRdg716xzeDJO2wWuiV5WTCsBwuG9+DTqb3Jy4gzcHA9KWXEe/jsrt7kt403ZIr15jBzMtdlec3vm5+JPiPXZ+GNUdh0Qyhi8O4TfTHNkrx+HL/LbiR5s1zwpUA35tBXE4tGNUFZvwTPb+l459vn14RmLhb/EYleUaFjktNIZA9d1xkQdoAOWahX3rtiK+mt7TSoosDXMdHBoeNNmjUpJn36h67P4sHrunHb6+sJh4VHZ4pXHL3sVPPi5h3XdCInzUu7BLeg29nsuG0SXZO8jL+iAyNfEobTuj7O0nU/E4lECDSECYRUxvZqL3BfoF3rGEYsXm8oBzplwdV/9bufyUn1MusfQkHxsRszWTisgIZwmL0n6nn4+q54bGCVJEIhhfR4Jwcr6pkzSPwQrYgVjc6oCYTgkQ+EX6lY5ov3J1zVkZRYJw2NYVp7HJS0i+OFYQU4ZLhpwVo2Hqli+5GzPPbhbuwShi3fjBuz8dglnh/WA69dJhJRGP2amOW4LOK6Ji7dZCyhfTF2lo29jFYeuzDfPhZg5id7qAg0MO3dnbT2OCjM8PPWbaWsnFjKj6dq+bGilncmlPLORMEDDwYjTLshk9mf7mfZrSU8flMOWSl+PE4rf3z2G3aXV5Leykk4otBn3ncMWLCG9YcrGbBgDR1auahpCLP7eDXVDUEWjhC886nLt7J0TDFZqbFMu6Ebd6/Yxv+9MZu6kEowrLBoZJFQ0AxF8DntzBncnZUTSkmPc/PqLUU8Mygfu8ZL31kuCrFHz9ay7Vg1i0cVGpCFjocvvfUy7l65jRqNgjVrQJ7heKUnLUVRmT0wX8AKmkaM4NyLweIPs79m4lKB/S4aVWRg2PpznjekwLAGXDSyCJ/TLqQoYkRn6/whBYaCpX4O0CDAUFNyv/bZbwynq1kD8wyT8YlvbKK6PsjekwGqG8TM3BdjP0eTRb+fBI+TRSOLDGy+uj7YzJRbh27MCdMMV5jtCfX9Jy3bbHTd6scwUxZb8nnVBxhz8qyqC7boYHWxf+uDmXlQM3/W0j4Xiguxaf7/SPLwHwLdnM9hSg9zY5PTKtFwEdNvM8wiI1YBjZd4m7o594V46ABuiyhsmsNrl6kLKriieOCdE51GA9U/GxmtrBw53bQWWTpaNEoVZ8Sy50TVOXBRUdtYGkNhQ1/G/AzNkJHPIdM1ycf6w5XGvn6nhUhEoWOix2jYKsjw43EIGd9xr25g3eGzWIAYmxh4zFGY4TdgmaKMOPacrKFTgpsDFQECjZFzoBo9PDZRH3lxeCFjXtto/A1XTSxl1OL1BBrDRNQmkxKXXcZqtRhm3D0f/xxZhtVaM5nXaSU7xU8gGGLnsWpWT+5Fn/lrcNlkJElG1Tp6c1N9bD5ajc9hITctltmD8/nD7K/5YHIv+sz/jkgkgiTJoKoEghHWPiigm2tmfcXrY4oZ9MJ31IdVitvGY7XKhqJiOKKws6yKzCSh4qiootu3qjFCbIyVDyb34oF3diDLktGVqifnk9X1xowdFaMRSndtMrsjRfPFdcaLoqiGeuIti39g78mAobaoK0B+rMEvuim4LhZmhlTMnPbr5nzbTB9eP58OAwEt7msu+pqv84/PfoOiNMFEF4NMDKhFVQ3IS99n7JIN7CirajZYXozLfinxr0IuvwZko8dvqmHKmdJZTR31LHMH5zNp+Vajc/R/bujK/3y4F7cV0lq5OXCilsI2sdzx+848/Y+91DSEDONwp0UkCRBY+N7jtbwwrAC3w8rNr25AVdRmtEynLCChF0YUEQwrzPzHbnaX1xlJsHuGj4iiElHFbF6XXwDB7lk65jL6LlhLlyQXMTYLsiQzc0AegYYwM1bvZLMJ885N87DrWIDOKS6OnGogNd7BwRP1tEuI4fiZejome/mpopbaoILTCh2TvBwor6FTiodDFXWEIwoNEQwJZRCv71m5nXcmlBoSD7MH5HL329t5aWQhnZM9XP3012SmeJAkCZskURsK88zA7rT2OBj/+gYAZg7Ix+e08ftZX6GoKrVBhddHFzNh6QZy0uOoC4Z46Posxi/dxNu39SQ9zs3YJRsINIawW2QevL4bI1/+gWcHdRcdt/O/Y+VtPem3YC3Lx5Vgt8rM/HQfKDCjbxYPvSv0+kf3ascD7+1g4fAepMQ6aQwrPPTODsZf2YG73t5K+9ZuNh+pwmOT+LummFjdEGLq8s0cqgiQmRoLKszom82Il9fz9zt+xx3LtzDthkyj+1Xf56b535KVGsuSMSXG+1Pf2soTf8mhpiFMhwQve8qraN+6CQrUlQYBDp0KMOsf+3n8pmweeHcHL49qssarbgjy53lrWDa2hNYexzmKirrBiN4B67BaqAg04HPamqlMAkZ366yBeYYtXmM4YmjYV9cHmfqWwLwbw5FmEgfRnaNHzgQM5Ufzterdr2bZA8DoXr2YuqS56cl8XrMmPZw7E47e3ryf3oXbUgdpdLTUBWv+zGxJeL5jXWh2faHzXkqS/lf3/6Xxm0r0OfkF6gtvf8K8zw+w7qezhlepPjOXwXCLQoGfz9SR4LVxUINhHDLYLBKRiJDw1Ztv9C7K+//QsRmX/EKhF29dFgERNagYHqO6VHJ2ipsHr8tqkimWwGIBWW4+kzdHtxQX+8rrjO5Q8wzbAbRLiqG8spH2rd0cMGnvx8jCcq8uIjD4unCInWWioJub6sHndFATDLHtaDW5aV5+qgiQlRLLvGEFlFXW89iHu9ldXmnMuoszYplxUzaDnv+OmpCK1ybx0dQr+PlUHROWbiSiKFgkqAk1STYUpvsJo3DkTAPvT7qcxrBCv+fW0DlRFI4jEQVJEh2jpwKN2K0yf372G+O6F48s5PkvDiLLEuGIwpMD8/jvZ77hmQG5PPK/e1h662UAXD93DSBWGa+NKWHIC2upV0TxNjPZSzASMTp6Fw4rYOKyzRRlxPH0YCEFMPmNzew+XkVmkt9Y1u06XklNUGXVxFJSY2OYunwrswfnM/61DciSxJYjVXwwpRfDX/qBjzVNdVkW7JoZq3czvU8mv3/mGz67S2jqHKuso0OC10imfeatoV1rF3bJwr5TAT65s7ch0ztx6SZkWSIYirCrvBKLxcLKCaXMWL2bYCjCnuNVZKXFsqusipqGCO9OFJ6z+goFoOfjn1PcJg5k2F1eRW5aHA/d0M0Y3PSuVHPyPHCymkEL1/HW+BI6JfpoCAltnx1lldQGI3z3wDVGstf3G/PKep7on8N9K8RKwOm0GuwZcyFXlzIwywSfrK43ZBXMuu0XawhqqUBr3qal2bzu6apfU3QC16WYWxo0LiY/oL9vvrfzRUuJ+0LH/dXYNL+lRK9DN9FsGIcGuehaMJcabptEJKRyqWCJzuQBaNfKyU+nm+/ZPsHOoYpzr0C3NDSul/PrzkOTKJv5fObQ2UVu0yBlDo+taTDrnu7nwIkqMlOakr/LCqoiOmp9TgvVDZFzjNVbusZotlP7BAeHKhrP2XfxyELuXrmNUChEICQ49qqqGoOILqVA1H7RoQ8g4lxNz9Zrl1FVlbatXfx8qo5ASDV494u+OUSgMcj2sgBOC4QU0XNw5HQDXRO97D5RRVijmnpsUBcS94UsU9MY0Y6tUB8WXcPbNUjKY5PITo0lrCg8OSDPkAnYVVaJLMnMHtSd0a9tFCbkb26msj5MSfs4nh6Qx43PrSUj1sGOshpirBKZKX5QwemwGrIJKyeU8uDK7Ww8XElBm1gOahr5e45XAyof3XkFpwNBRi3+AUmS6Jjg5mBFLdkpfmb0zeKPs78hJ9XLoVO11DQqvKVJTrw3sZSZn+xjx7FKuiX6ePHmIkM756qZX/Hm2BLGv76RT7TV0LhXN2C1yzzRL6cZJRMEw+W/n/kKSZJRIhFqQwqfTO1NepybMa+uBxVjEJBliSf659Bn/nd0S/Ty1MBcbpj7LbIsGzrweoE0OmHqGLqiqOdAT2aZhCnLtwA0W1mY99fF5/Rt9ISvH0e3k4TmMgVmBtD5DLp15c3sFJ8Be7W0GrkQtGR+72JaOP9q/KZYN3pE5zYdV49OsS3IujeL1j77JSd5aJ50zUle/7PoicgWtZ85ycOFk7x+IrvccpKHJgqpErUo0A3HAyHV0L6ZcGUHAiHYc7zamOHXhZu0cdq3cuG1S7ij9BuirzHGAhZZMuQHAA5p3bjR0g9TV2xlVv88I7ErikrnpCaRrp/PNJCX5hUJlnMb4PTn99znB4zzmQfQNq1iqAsJpU594Gmf6GT0axvZdvSsUW8IRkSy/vlUHZ0TPSDgc6OfYO6QQvLTfVisFp7XZpftEz1YLBbDglCPupDKrvIqNh6pou+8b2jfysX0PlkEgiqhcMQYkEYuXm9AOdP/LHRkVowXWvZdUzzUhlQiqKw/UsmD13fFapHpnOBh2iqhjV/Qxs+zWiMUoNVIJO5ZsY1+C9bSKdHDygmluBw2MpN9wtRj9W5y0sSg1CHRiwo8/vfdqMDfPtpDUJv5bjhylopAAzvLBVzocViZ/dl+FFVl8rLN3PbaRjYerTSSfDTD5d6V28lJi+XTqb157dYSitrGCc39UIRgKMKOskpumLuGneWV7CyrwmEVLJ2d5ZXct3I7qqqKVYitSQN+Rp9M/jD7a8Yu2WB0u+pOUGYDbr2oChiSxnMHdxd6Om9u5uqnv2y2PxJGIfrRvllMWb7FkFWYumIrj/bN4ro53zaz7YvmwevbmlcX+mud5aMnebNUg5ndcyFmjfmYl9rE9WvHf1SiP1+Y80W71s6LiolFJ+B/NvSvQZpf/JFCtKyzEx0um6z9X2qW7BppLuvQUtijhMwAkcyALK3j1mWFJL9oxHp6QL6xnd5oNntALh6nHVmWWTyqiBjtr2xFdMJmJjfp00tA+wQ3HZOa8Ok2rRy4TDUPPVLiHNy9chuZmr59bRgmX9PZ+Pzh67vyY0WAuUN64LKIhrFOSTFa05mDEGLVgiwGJQkBkXXTdPl/rqglM8WNz2GhS5KYkR082WAYmOiRmerm54paqhsVFFXIPH90Z29Dw3/hlz+y7Vg1HRPcPPf5QXLTvPxcUUtWqp+Xbi5mRh+hveKxS7jtEm/dVsrS0cXUhmDz0WqmrdqBCnRN8dFdk0XwOCzkpseyaqIwGdeTam2jcCDrnuFnzuDulLSLo0OCl1kD8zhwqhYlotIlycvBilrue3s7M25sOrcsSzw9MI+CDD8HK2qZ/v4upv85kxdGFGKVZab3yeRQRS0FGX5skugq/vFkgMIMP9P7ZHHwdB2LRhbRs2Mr0uPcRqdqXoZocHpn4uXsOxlgztDufPfA1ficdiNJ6aHTC3Xc/6YFa0EVhdfGcIQtx6p4c1xP8jJieff2XuRmxOKLsTNnSHdkWeaRvtlYLKL2YD72X99r6kPQk//CEYW8fLPobtWhoPGvbzQ6X83aNA6bhUUji/jC1Emrs4ycNuEa1fe5tUy7oVszWYWMeE+LtEv9XltKvtHSA2bTkOiGLPOxWoqWtG5aWjlcLP7dMgi/iUTfDHqwXLqSzPlE0VoKU3MqTplmZuTHqpoeeoSWRc7MkRIr5q7BkMp5eqfOGy0NBLqA095yMaOtC8NjH+3Ba5fJSvMZs2MdfZn69nYmXNkBVVW5dckG6hXh2hVGCHl57E1fvLYJLrYerTHUKAEOn240Zsd6R3DXJDcnqoK8cnMRe8vr8Ngk8tN9PPfFAWO/B97dSU1QdK/q+5+obMRlg1NVIVEw192o0sV110VgvybzXBsWAmptWwmZaD2OnK6ng0Z/BaGFo7nBcaBCGLPf9eZWxr+xmbatXTw9OI/L2sdjlWV2n6hm+7EaJFlijjaLfPLjfaLn4LbLARjy4vcGdbRHGz/TbshCBg5W1PL0gDwKM/wsH9+T6oYgAxZ+T2M4whd3X0l6nJvubWKREA1Tf313J3YNW0/0xfDJnb1FH4css3JCKXtO1HDT/G/ZeLgSSZLpluTDYbXw4+k6Vk4oJRxSuG7Ot1Q3iFXOtFU7qW5UsMoyVquM12mla5IPu83CsJd+YPGoQoa+tM4w035AK3QvGikS6SMf7iErxWfIDV8z6ytDoyVak2Xqiq0keGP4x129eXqQaKbyxdhZc//VZKXGMn9IAY9/tNdItA6rGPjS49x8cmdvHnh3RzNsff6QAr66V7hbmcXKzP8tHFHI3MHdDXqoHmZpg2jcXqdPOmwWPtf+Bjlp/mZ4vZ6oz9e92pLRyPnw+uh9LxQtadX8Kyydf2ey/49M9BmtmutAtksQf5T0OCt7T9S3tEuLEVSEqfeFQs/vOmPToeHkXRJFE5AvKqs7EBzl+YPFTNoOBlQBwrdWtzgMa8fX78YaNUa1BEFlppx7vT9WiESYrTldua0wo08WsixxOhA0On8fuTGT3DRhtzfxjY3Ikkx7zS3q8Bmx0U8VdUy4qqNxbJfNRkn7OFaMKzFcvNq3tiMDT/XLpkERyb7sbB2rJ11O+9Ye8tN9BEIqhypqeXJAHh0Txeqiu9ZoVWBywYqEFSwWC21buYwVgqKq7NcKzm4bPD1ANPHkavfnstvokiSew+KRhSwfX8rPp+vJTfOSm+qha7IPSRJSEO9MLMVuE1op2aluth+r4b63t/NI32zsVgsfTO5FYYafDyb3Mnjs91/bBYBp7+5AkiS6Jft4dXQxPTvEE4ko3PLqBgoy/MiyzINvb2fjkSr6zFvDfW9v5/3bLzfs5kD8kHtk+I0Gq/lDC5r9uBVFNUy0V0+6nG7JforbxdE1yYvVKhtiZA6rjNNp5fO7r8DntPN4v2zuv1ZIe0y/MUuboQunrzmDu5OT5qd9aw/F7eKNmbreAKWfX5+pgxgEVt1eit8l7AGBZtx3fbsZH+zmz3O/NXjtetHWYUqQDaEIk5ZtNpJ+oi/GOLeuCzN1xVYcphl0S0lLb3zS8XOznow58eqJr6Kmnp3l1QZ3X0/uOi2zpeO3FPrM/kLb/rubpX5J/BqNVZec6CVJskiStFmSpA+1f7eXJGmdJEn7JUl6S5Iku/a+Q/v3Ae3zdhc7dox2Q3riM/PFAX7ScNyjZ8+tUF7sUZRXXriMGz3h1mGhAycDJMc6aduqOVWrEYhEIjz8/k5y0zw47E2FzBgZTlaHeapftrG9Q2qqPUTT/1uCoH4srzPuSbcH7JYshMlCivhy1oZh2ns7iEQijHipybPzofd3G0XGrlry+vFkc3mESCTCnW+JYpdE06x15sd7jUR86FQQt8PCo/8rGrwaFKgLqjSGFSa9uZlDp8WMXpIk7n17m2Hq0hiOMO71jUzvI+5fZym1T3Ab+LrbKoyn67WHURcSKxAJoZkCMPmaTob0wh3LNzPspR945eYith2rYXtZgD2anG9mihDvevymbGRZ4uiZBrwOmfuvE9rriiKueeORKu5ZsY2JSzex7Wglf5ormq82Hq1CVSEYjjBg4ffcflVHth6rYVb/PBbfchmfTu1tyBGvnFCKbJEY+uL3nKyuZ+ySDVTUiEnHHg3jP3QqYCS1k9X1/HH21yBD5wQPf5z9DVPe3MLmo5VM75PFvpM1xpdv1oA8+j63VmivO+1cOfMLrn76a0a+vA6vXeaxD/cwcekmBrzwPYoqlCp1WqSMxKRlm41C5PjXN1JVF2T86xsFHVOzBZw9MJ/pq3dz5EyAP87+2sC3zbNwp83C4zdlG78JHZseu2SDwYcHMUDo0JW+jblpy4yH6wNQS/RG/XVLcsLm7fVjPLxql9EH8K8kQvOM+UKz5ouxhi51n38m/t2Y/i+Z0d8B7Db9+0lgtqqqnYGzwBjt/THAWVVVOwGzte0uGG3iXbRpbTOkRnVZYf3/j90ovFY7JMTQvnXTkktGQClum2R004LAfIvbxpKb6mH5uBK6JsXQrrXT+Nz8CB2SYJnoAmJdkl04EEmw33Nr2K5ptS8dXWzsUxuGFL8dGcmQbbADWWl+5g3uziMf7sKOcLxyOCw8dmMmnZNi6KZJNpjXK0/1y8aM4DUCcwbnk53iZq4m+6Bf8+7y2ibdeBUiEeiU5BHdq6Zj5KR6CIcVRr68jqzUWAozxEx76ehictLi6JqirQxsEi+OLKSmQWjXuK1CIO6zu3rz/LAeZLQSz2zhsAJy0rzMWL2byVd3ZOWEUjJTNC9Zi4WcNA9dkmM4fKqWzFQPZwJicG2b6KIww8/jN+VS0k5IUmSlxvLM4HyW3FKMxwbPDMjFbYWiNrH8nxsy8TkstG3tYtXEUuFGleTl7dt6khobw1vjSshP99Itxc+S0cXISNz2+kb6zP+OGTdm8ea4nkTCCiNfWoeiKNx7bRd8Thv5aV6eHpjH7MH5vDOxlJ4d4lk4rICPpvTi+eGFHDpdxyujishNFxISt762kduWbuRUoJF9J2v4ZGpvUmNjeKJfDhFFmHFvP1rJn+Z8w5he7agLC4rtzE/2GTDKvSu30y3Fx5zB3XlueA+6p/v56bSQZwCM65uyfEuTHK9VzHC/uvdqvrjnCv73jt6oqsIT/XN4+ZZiPrmzN7MH5RtiYZOWbQZJrBocNtEBO7N/Lg6bhXBYYfLSzfz1+q5cN+dbAP56fVceeGeHMVgoimrM6vWkp2v83LF8C2OXbBASwrLEo32zmiWfFeNLDBy8oqbemJUDBjwEgsVintEfORNoscBpfP+jBgH9M30FpcNQ+qxcp1S2lIDPl5TNK5gLyRFHh1m+2HyPv4W4JHqlJEnpwBLgMeAu4M9ABZCsqmpYkqRS4H9UVf2jJEmfaK/XSpJkBY4DCeoFTuRM6awmj3qWZC8crznfVuePlkTK9GYrncqoC5VdKmTeKcnJ8TONBLTZemaSi90n6i6y16Vd2z8bnRJclJ2pE1z+VLegVGpUQmhqAqsLqQhOB7w+upixr643Crw+h4WH/9SN+7QfNJzbBRwtvqY7PmWlutlVVmvQQIvbxLL3eDXVF6gw60VdHRJTEXr01Q3Nn0qm5o7ltktYLBY6tnKz8WiV6IDV3KTMocs2Z6X6CIcVDpyqJRgMCRtGu9bFqlkX1obBa5OQZBlQad/KzdayGkN8Taec+p0WPpzyO26Yu4YuiR42HalEUeG9iaUMWPg9eak+Nh6p4qMpvZixehcbfz7bbJVW3CYWl9Nm0AQnvrEJvZiwq7yat2/ryUPv7mDdT03OVEVtYnlueA/uXrGNneXVfKEVEse8sp6HbujG9XPXUKxtc+/K7YQj4iGanaQeXrWLR/tm0Wf+dwB8cmdvbl+6ifWHKyntpyx2+AAAIABJREFUEG/UJkr/9jlFGXFY7TILhgo7RKtVNnDyBcN6GKsAVOFctWhUEdX1Qf7wzFd8dd81AFw180tqGkN8cc+VAIbQmy9GwELXzvmWzCQvswfnG41fejfu5U98zhf3XElGvJj06AlVp2PqnbeAQbM0d79GwzmzB+Yb1MyWMHY9oZtVNM3RUjOVuZu3pfMBhqDapRzz14x/N73yWeA+mvJBK6BSVVX913oUSNNepwFHALTPq7Ttoy9wnCRJGyRJ2qD/VqKTvHkWbo7MFFezYml0In3ous78z4d7wXTBYZoKi+Zo26r5OfRNDpwQcIQFMfONTvIxVrGklxFdubq2e06qr9l25mvT6YXRuvjpsU3ETbeVc/To9RqFzSIbRc45g3uwamIpX957jXFOq1XGYrEIMbbRxciSMAvRk7wEZCb7+GNOKl2T3PidFgrb+Fk+rqTZ+Z4Z2J2sFKHF77FJdNBw/rmDe7B4ZKFBA33ohkzy28azeGQhMsKzNrpW3jXFh9su06BAdpoHiwSvjS4W0A+CIWSRxGolN81LVmosmck+ZFnCKkNeho/akEp+ug9/jJXiNrF8NKUXJR3i+cddV/DC8EJcThuf3NmbL+67hoIMP6sm9cJqtbBqYik56XFYZXh9bE9kWUaWZRbdXMzikYUEVaGuWdSpFZ/d1Zuv77vGYK08N7wHPTJi8cdYmfnxPt6//XKeHdIdv9PK3/6+lxdHFvHx1N4UZIhnn5/u5aWbiw2HI4fNYujeLBjWg0/uFLx0u81CSTtRE7HIsO9EDXe/LZqNPtaEyKrrg6z76TR2q4zPacEqy9y9Yhsz++ciS8KwXJ/BT1+9m0f7ZjF99W4+ubM3X95zleCu2618dldvXrnlMlFTsFrwxdiYN7yAl0cVGxrwcwd3NyATEFj+gqE9DHaMnrACQbGS8bvsfHnvVXxxz5VMX70bn9MuJIW1mfYD7+7g/dtLsVplfDFCeEzntPtixLa6Tg40QRQLhvUg0RdjMGZ0mmW0yJh5Px3uiU7y5s8Bxi7ZwNWmQnT0dtH/vpCuvM4IulCS/7XMQ/6VuGiilyTpBuCkqqobzW+3sKl6CZ81vaGqL6qqWqSqapHLZsHvtOK2CihFv6jT1UG6pbjwOSx4bAKvLkj34bHZscoSHRObbPa6Zwjdc4B5nx80jCme0RJJx0QHsiy6Wu2IZDp/cD6J7qZE77ZJvKbppuemeshOi+XNcSW8P/l3uE3YyOKRhRS1b80Hk3uxfFwJDRGhvvjJ1N647RYsEnRJclGQ7iM/zYvHJuFzWHh/Si86JDjJSRPv6wPP0cqQ8eCeGVRAYwRy0rz4HDIuCyS53HTP8BEbYydPK3Q6rDKpseIH43fayEr1kJns452JpcwbWsDi737my3uvZMnoYrwOCxKC0vf0oDyOVdbR2u2kS6IXu2whJdbJ0tHF+J0iOdqtMuVVDSwdXYwkgdNmpaRdHOlxbi7vnGDAYk9+tJcHr+vKnW9txuu0MH9oD96ZUEpJuzgWDisgNsbGY/1yRY1BgsOn6nhzrJBFqA+ruG2wclMZH0zuxaqJpbjsNmQknhqQi1WWWT2pF0tuKaFnh3hcVisfTu7Fc8PFj9euyQk4bEJES09mP50WA3JueiztWwvzjy/uuZJuKX4+ndqbT6deIXjYX/2IL8bKvCEi8SV4nPhdoqN10cgiQRe0Cz0aRVEZ9tIPPLByB2+OK2nGann1lhJWTSw1mExmZUazyJne4DN/SAFLxpSQlxHHdw9cw9f3X8P8IcKoW2euNKlkyuSmxfLUwFwDItSNNxpDEaNDdfrq3YbSo34PsiyRHuc2vrN+l50vNQMQHQPXk7x+vdGMFD3RJfpi+O6Bq43CrN9lN9g0fpfdkITQsfSMeI+RKKPZLfq2euiJUYdszIwZM82xpWhplh/9udNmYdGoonMUMM3nb+m45yvu6td7Pt37X6OQ+u+Ii0I3kiT9DRiBNikGfMB7wB/5N0E3rrQu6hurPzN0XO7+r/bM+n+HiJGgXhUyuT+fqiMYjGDTioDtEzwcOF5tdJDmpnoY3as9U9/eLnB2WWZ7WQC3FdJbu9h7vGXYJTPZxb4TdcwZJHR2itvEEoxE+PlMA42NIeoVMTgI31SJ+pAqlB6HFzFx6QbaazZzhel+HumXw4xVuwihsOmwgB3cGuxRkC5mqXoLv97tK1YGEuGwami4u61gs1pJi3Owq7yWHhl+Hrkphxnv7yIcUQx9+YEvrsNnF8yarcdqNLNrmdz0OGbcmEWgIcxNC9aSk+rh4IkAkgTtEsS9ZCe7+fFkLRYN6gFw2yW6JPnYcrSKnFQviqqyvSxAcZtYZvTNpkOCmOWPWPS9IYLmd1ioboyQl+7FKslsPlJFbqqXrWU1LBxWwAPv7aBTa7chQrXhcCUqQhLiSEUd3VL9HDgZACTN2Bsyk/388PNZ3Db4+1SxjP/TnG/onOBl74lqAiGVFeNKmPd5k6zFrIF5NIYj3LNiG3tP1LByQimPfLgHRVUJNkaw2y1GR+b913bh+rlrWDyykCXfH2Zm/1z+OPtr3hhbwoiX1/PW+BJDpmDesALuWLaFR/plM+29nWw4fJY1919tMHge75fNDXO/RVVUvnngv5p1aeoY+vwhBca2D6/aZUgL6EJful68rvmi69jf+NxaOrZyaTTRGkDlg8m9eHjVLkNT3ixlADQTOIvWoofmXZ7mjlGzRrx+Tfr1Re/TkniZngTNEgQtSQ/oEd2Zq4ui/ZIEab6e6vrgJR3DXHw+3/Veynkvdo7fHHSjquqDqqqmq6raDhgMfK6q6jDgC6C/ttko4H3t9Wrt32iff36hJA9gt8hMfXu70fAz6/8dAqCjBh9sP1ZDWryTBhVqgioZrVxsPVrdTCZge1nAGCj2Ha8zWB61YTh0niQPsPt4HV2TPUxaLgpJO49VsvVYDS+O6EGDIuCYnRrPuy6k4pDFNQxfvJ7qoMq+MpFgGyJhrp+7hh8On6U+KL5MHZJiBKYK7D9Rw/3XCj36GKtI8m67TEMYIhG1mfG3RZZ5cWQhO8trUYHNR6r409w1bDh8lt1lwiDj1iVClExRFX4+00Bxm1g+vau3MNaIKPzhmW/ot2Atr48u5ufTAtfXeepum8TO47XUKxBRVDwOC/kZfrol+9l3vJq8dB9bNYaL2yax81gl189dw6iX1hnUPBBfnue0H/rOsho2HqnC42iCLMa/sZmMeCd7T1Tz4+k6ZvTNNrTz95TXURuGfcer6ZTgoVOCaIqSJInpNwoeeyAkEvxdb20l0Kiw9VgVtSGVvDSfSPKSSPB1DSH+MPsbrnzqK8IRRUgvr97NzP65PH5TNj8cPiu6VbUkP/RFwVQa/dpG6hpCNIYjKCoMWbiWOYPy6f/8WhqCYTYcPsvtb2zi+5/O8NDKHbwwolDQH2PsTFq2mWAown2a1HNtSKG6IWgkeb0LdNaAPFEcjSj8ed4aguHmhcfGkDD3nrh0E3989hsaQxFmD8wnwRvD+7eXsr8igGyV+GDy5WQm+3h41S6m3dANJAyPVT109osupQwQDiuGKbfepQrNoQgd5zazdfTri/Y7jW4gguYFTX3loSdhvYCpH3vMK+ubFU8bQk3a8780OerX0BiKcN2cb5tJHbcUemLXz6+vInQ9/0uFXC52jujj/CfAOP8Kj/5+4C5Jkg4gMPiXtfdfBlpp798FPHCxAwU1/XabvfkDnHx1J+O1VTNksEiwU0viHUzQzcJhBcZrHRJxaRaEF6qPP3ZjJjFWi4E3RbS/ydnaIO9NLOWDyb2awTY6Pu1xyGSnuKlXRav/zvJaAem0ieXIKaHHcuBEPfUR4YYlScL8A0SrP0CXJA/d033N3LMAIoqCx2nloym9NOlcISuw5JZi6rRtXxpVRHGbWP4+9Uo+ndqbv/XPxee0sbu8iqcH5vHpXb3xOGTiPeIHv3ikkAUozPDzxtieRm3BbrPy2pjLOHSqFkVVqQmp3P3fXfE5LHgdFjokurFaZfxO4YR094pthFVxEfkZfp7/8keK2sSy7NYS/E4rmck+Zg5s6tbdeayGrkk+3r6tJ3/7aC/vTepFaYd4ow+hc7KP/SdrOHiqlldvKSYnLZa0WBc92sSSn+5l+fhS7BYLX913JR9P7c1l7eLwOm083i+bRSOLqG4IseloFS+O6EFJh3jsNsFL1x2UahrCqMAjGpY94uX1vKrBcyvGlWC3WvjrezvpkugBSWb44vWoqsozg/MpahvHM4Py8cdY2VtRQ2NYsFIaQxEURWXPiWqQBEz05tgSpq/ebbBMHu0rOmDvXbldbB9RyUzxYbXINIYjPNo3i0nLNjNl+RbmDylg9uB8w9x70rLNjF2yAYfVQnaqX+jPO+3YbRYe7ZvFoIXrDMMSnVKpJ5PZA/MNQxAQdRtdf15R1XMolUAzGEJRVO5esa0Zdm+e0Ufj1ubkr0NG+mdmyqTfZWfu4O5GXUAPMyXzUiN6ZaCbkESrVkaHGX8HAbPpPrX8f+S9eXgUZbr+/6mq3pJ0dwJCgGwgexKSEFYRcfuOyzjKIAICKm4giAgi7g7DcBxXxIgCCgiiAiIgIsMw6sxRERFDhBBICGFRIBtJWJLuTtLpdFf9/ni7KpUQEOecOeNcv/e6uJJU1/JWJTzvU/dzP/cN/yuQS0vo5teC2f8qRM0i43pol973On++Vcjw6kwZHd6QgO5hTfeFYzNY8s1R/KEghyuadGl6dYyg6KTgNXdt7+DHKj8RrcgJ6COprcVoIgJIamflxCmBlZtFx9I6OTl80kdAE4VdK9C5g4MXfp/Bi1sLm8kRu6wSXWKjmnWZmlUqF4ZlmI37liE5XljbhYCGYCOHK/z07BjBoZP1ZI1O49mN+1EUAf+se2Aw96zIpi4I/ROiOXLKR+9YNwE1RG6Jh5Q4JwfKfAzp0pbnRqZyQ9Z2Iq3NrQ2jHQpdL4nCFwxwuMIvjLORCWoqM6/ryZ0rcohxWOjRwUkgGCKv1MuKCf1JaheJ22HF42/kute2oyF05wGKymuQFaFrL0sS913RhcmrRadpaqco4VSlQWF5Nb06RjN7eAp3r9hFjT/EpqlDeH5LoWiiqvSSGh/DK6PSuCnrG7yNGtEOC6Dx4QOX8dzmQvzBIDZZoajKy5pJg3n1i8M8dn0PXv3iMHOHJ/Poh3m8Ni4Dt8NGQzDEjDV72XnsjKE+WeXz097pYMLy73n//suYtiaXOcOTeXZDPrNu7Mmk93fTu4OLN+/I5Pqsb9j2+DVAk5Twda9t45snrqXK52fUWzv5y8NDOe0LMGLxTrZOH8qLW4uYPyadWev2GdBPQzDEtfO/4ctZQmDs+qxvCAVD9O4UzaI7mwxJ5o9Jxx1h4/53cwxNmGCjytJ7BhiZuq4drwuT6Rm6TnXUPVb1QqpZPljPYFuqXZqDUJW3nmc2FjQrxEJzqMeM65s/n/zBbv48IsVg1LQ2auoCzUTPdJZNa0qUrcEpLeGj8wXmi4FPWl7jfEJnP3fefwbK+d+Cd/6j1Csj43posRPObzxil1tvLvq/HGbfWvMwe9KmxEVRWCbgFnOA/58OXWc+ytYUtAcmxVBUIWhKupesPnR/V199ABWN3OKmxajlYtO7o5ODJqcuEG9HK787TkFZDcFgiLoQYatBmdROMewvq8YXUImyQH1QLICRCiTHRbO7uKbZuZxhiWeJ5hTOKJtEbUDDGTYuiQ5TLgckxSAjkXPiLCqifyHrH4fIPSEsEUMI+YSjVT48DSrbn7iK9q4IPPUBHlq9h5zj1cbikNIxGn8wSG6Jh/QEFydO+/HWN5Ia72ZfqYd1Dwwm6++HKSz3UO0PoiC6eyNtVuYMTzb8WwGmrtpDUFXJOXaWLx4dxpgl2fRoF4UsS+z86QwgFr6jp2r5YuaVzFi7l5xjZ+if0AabTWHO8GQS2kTR0Bhi4socck5UowCDurRFViTyS6vpExfDS6P68NSGfIJhZbtdx87y9/AipSsz6vRFnWIJTSqOuvrjkrv6G7i1jtdPCsN9siwZcsM6Rq972xaUC6OUd+8f1CwQ6brxul59awvP/StzOFDuMYw/Wi4k+tzMipOVHpGcmfH189UPgGbnOp8qpHkxuNhgWlMXOIcyqS9KrdUozrftYheY/y0jkv8o9cpusU76JcUA4LQpRoOU3pJvtypcaqJa6tTK7h2atvXu6DTkCmREQdNplYiyNt1iy7U60iKRmejGbVcMhUgQjByXTSYqzJZJjXNitwn8Jsqu0CfeZYhdKbKMI3xohMVCv8RoFAkcNtlobnLa5Ga0w7R4F267QkrYh7ZPvFPMOTx/XVIhpZOTvgnRzP3LAVwOhY+mXI7bIVg9L45KIy0hhl4dmiiN3WOFbMDczYJX/eOpOg6f9KBI4pzRDgsf7iqhf1IMvToIRsbBkz6iHRZD7kECVu44Hs4oZZLjo3E7FJw20Yk6e3gysgQWGVLiYujXWWi91IUE73pg5xgDh9eZQIO6tGFw17ZsmjqEgZ0FPVIJQ3FvjBOv0csm9Cc6wopFkZEtksD6gbvfzeFIpY+NU4fwl+lDUSR4fmQaPTqI+k1DUGXyB7uZ8eFeLJLM1ulD+fujV9InbNQ9b0wGMnDidD1/efhyvn7iKqIjbPRLiuHNL49ischsmDqEmAgL/TrHEGkTsNDot7+nV6y4xtRVewxrwcFd25LQJoqvZl3FexMH8/Zd/RncpQ0DO8fw1l39SUsQol/v3juIrx67ShSBw0qU96/MYdqaXN65ZyD/eHQYl3W/hLcn9Cfr9gxkWRa2iQu/I6ipHKr0MWd4CooMo97aKeoIqoY7wsZnM65AlgXF0qwXo3Pg541KM3DrucOTja5VWZZYOD6zWZCPjrTx0sg+/H6R6CTu1cGFxdo8LNTUBbjm1a+ZuS7PaM7Ss/EDZdVc+co2qrz1LL6jn6E7Y8boJ3+wm0nv/cC0D3MJhlRjcfI3hpi1fh83vr79HCtBc/1AZzKZGToXUoX8Z5gvOvxjDvLXzN/GpPd+aLVG0dq1Lham+Xcwc34lGX1Pbd3WL5m2No9usQ7aOSK4bUAcH2QfZ3+pj7Q4J/vLfHSPjcCuyFgUhX0lHhwyBFWwWWD1A0OYu7mAg2UeesW7OVzuYcmEgcz/ooiDZR7mjcmga6yTpzfu49a+cczdUoRdgi0zh+HzB3nxrwe5rX8cf/ykgNTEaA5XeHnn7oE8t6UAh2Lh2VuSuWPpTt69bzDzPisCDYJoHCytQVEkQqrGxw8NxWaROVntZ+rqPaiqSud2kTx/a7qBz5/xBbBZZOZ/fohAKMTTNyXT1mnjyfX7ePK3vZn3WRFzR6QSCKpc4rRhtyic8jUw+5N83rqrv2Emor/ye/wBvP4gP1b6+E1qJ/YVn6VLuyhi3REcKKtm3NLvyRrTl5nr9rJofD96dnTREAzx6No88kuqkWX4dPow5mw6wOM39mTC8mzWTh7Cc5sLmT08mbmbDvD0zb15fkshsixxuNLH23f2o63Tht0i43bYmLgyh0AoxIu3peNyWDjtC3DGF2DGWqFb8tGUy4mPiaQhGGLWun3MH5MOQFl1PXExEeSX1NAxxoHNIhvn3H3sDG9+eZiHr+3Bgv8+xPHTdWyYejmPr8vDJisgCzz5g0mX8WOVl/HLstE0jW+euLYZVGG3Kty9PJtXx6QbkIKeRepwTHtXBCVna2nvdNAQDOHxN/Lsx0IgbOndA5i6ag/P3tzbgGV0potx/mXZvDo23eCHt0b30ztZVU1rph6pW/LpkJJ5Xnq2rDtM3f9uDovD9NJpa3JZOF7UpcyZvM6p14uUZiaNp144TJmzcX3oz0Q3EGmZyer3am48Kj7jI7GtkyOVHrrHuo3zmLXg9axdp8K25lDV0hWqNbjGzCrSn1lrXrLm53++cbGwScuM/mLO8X/JuIH/MOjm5zxju3eI4EgLMbMIBerPs3BejFGJbqqh67G0vF5pVX0zfL+16+nGKK2NlmYe5mtCcwMQ83aHhFEPUCQBAemMHHPNIdou072Diz1huqZGEyzjtsv8dcYwbn7jWxoDIRo0sGjiek6r6DytMXWmrntgMGOXZuMIPwvduEP/WQrPcXDXtjx5Yy9GLN5pwDZbpg9l7Ns7L9gdq0jQLyGGI6dr6do2kh/P1NGzvZNdx8+e02AhAz1bgZNcdmGCXhM2U3HaZPrExzD7lmR+98YOBnRpg02RWX7PQAOLnrYml6dv6sWYt3aQ2bkdy+4eYCwKakjFF1DRgH6J0eQW1zAwqY3hSOW0StQHNT6fOYzbFn+HJEkkd3IjIxkmHEVVPlbc3Z8Ri3fiskr0iW/DgvF9DXcoHabQA5M+J1XT2F9ylrT4NoaJh6e+kUFd2vLcreJtok9cU0FZ78a8at5XpHYS7lnBRhVkKDrpIS2xDfNHpzPzozxkSWL+mPRmzlM1dQEeeO8HDlX56BMXzQsjU43FAMQCoHvMIgntHbMHrJlqaaYz3vj6dj6ddnmzheWaV782fGj1BeKa+dtI7eRu5kBlhnTMMMb5jDrM++osId1gxEwJBVptoNKH2eTk5wL1hb6/WOjll9QK/pnxHwXdRFoVskanERm+18S2YqXWb92myAZLQ0LAMq0FeV1LxmrB0HbJSHDRs0PUOfv26ujEIQtcWUdVuoZVGI9U1DfrJo00BflIRVzfZVdo0IRBddN9iM/6JrpBPXcF6BFWZIyyyfSMd+KyyWTEu4xO2Fn/71LRkh9u7EqOc+LXmjrQusRGGrBO1u2ZHK2qZUCXNvSJcxJpgYfD2Lu+dquqRp0qitMNCF0dRZF5LwzTpMW7DD56pK2p67ZHBzeRVglFkYiyCOnebU9cxdt39ufSdk76J0UjSxKRVnj+rwdJ7hRNZmI0qZ2ElHDvsLa9Dr2ldHJisyksubMfR075ePeeATz9O0E1XTg2A0USgXvT1CFkJkZz6KSPnh0jjYC+aeoQts64gkXj+wljcAkD6hi7ZCeKDK+NSWf5PQOp8tZz9atfM+X93YaAma8RZl3fgypvPTe9sYNu7aLo0cGFO8LKJ1OHYLMouBwW5oxIIaVTDP2TYkhLaMPnMwU2ntIpmi9mXskro9IoKK/hljd3UFjhYd3kwbz8WRFCWEHjwEkP12dt58pXvmTqqj0EgiLLrvTUc82rXwOi4Wnu71OoDagEwiyezx8ZxtePX4WsSIxa/B29O7oMF6W5w5OFtk0wRFqCwPABiiq9FFX4SO4UzQu3poqGrLDuzcyP8rj61a8NQ44p7+9md3E166dcxsLxmUaDFcCk93/gxte3M29UmtGM1ZLuqFMmzZTKmWvz6NXBxR82HTD2tVsVkuPcBu1TbyzSjTx0KMYMcwDNAn9rkEzLfRuCIYKqyo2vb6fSU298psNYFyrQTl+717if1j5vTYenJSTTGvRyPq2dn4Ny/q9YOb+qjF4PaPqMrFKTxrqeATutEkntIzlQVkuEIuiQAUQm3Ds+mr0lIsM1Z8nJHSMNNcSWY8kdmTz6Ua7ByY+0hL1iVUjp5OJ4lZeenaLJK66hZ6coXFYrhyq9dI91cbCsmjfHN9ni6Rl3WryT/aU+eneKJEKxhH1VNWOxSA8XA9PiXfxY4UVRZNpHWzla2UCXdg6OnRJsoiiLhCRDanwMdw1OYtraPJI7RVJYXkf/pBhkhDm24bUaLmhmJLh4/77LmLJqN3WBRvJKmrQlXDaJlI4x5JedpTYIMREW1kwazPhl2Sy9qz/3vbuLHh1d5BZ7cNmFZowsyWyZcYUQvJLA7w+yu1g0TH3x6DBmbypg34kzNKgSaXEu5o3J4JGP9nKs0gdoKLJCz05u9hw/SzhpRAv/UyRYdld/Hvkol7TENgSDKgdPeuge6+RgWQ2KIrPxoaHctug7ahpC9E+M5vVxImueuTYPZCHZ3D3WTU1dgCtf+ZKUOGECcsrXwNglAnvOSGxL1tgMHl6Ty6tj0hm+8DtWTxzEi1uLUNEIBEIcrvSCJJPcyYWs/zXKcKDMw+Zw5qqLls1av89ohPI1BLDJisGweeaTAuaPTjcKl3arwv0rc5p456v24A8GcVgsyIrEwnGZRtavm5LbrQqT3v8BNSQemEWRm2HrDUHRQdsQDDUruEKYn28qnOrNWmZ9mZawjBkGafm57hWrwys6fv3pQ0POsSVs2ZDU8uv5CpfnY9m0nJNeNDWLwJmZRT+Xaf8cvHMxGX1rx1ysteDPXfOXjv+ojN5hbSqumvVozMiHDnPEXeLgQFktl7azUR9qgmj8GowflGDs3yvsWvTKyFQjyCtgFE71MXl1LqEQJF0i9Gbqgk3smmOVQjM9qIaItEocLK+lLhikZ6yLYChEbRAjyEMTrHIgzPM/WF5HUbnHCPIR4Wy8ul7I/B4p18+vcbRS3KEe5B0K1Ac1esS6mHNLCtPD2bpDsZAaF4VFFl2mNotMZmI0brvM4vGZRFkgr8RLaXUdalDjxZHpuGwSvcKOTZfGOgmqqrGwdWvv5PktB0kOK1r6GjUKw5TRhLYOgkENVVN5bO0+VE1jzi3JOBwWUuOcQmzMHyQYFOdbemc/jlb6GLloB/mlXnyNGpe2d9K7k5u5w1P4+MEh9EuMpn/nGHqHdfcXj89k5jpRnHv2pt5YZJkPH7iMI5U+8WxCKs9uyKdnBxeZiW5W3DvICHAgeOPPbTlosEK8/hCzb07moVV7GL/0e0POGBlmrNmLzargdtjoekkkz4c7Z+cOTyHSYWXD1MvZ8OBlvHvvICNzzhqTweePDGsmMxDrjjCC85zhyewv9VBQKoTKntqQz8JxmcS6I1hyV3/sVkU0IGkCdpgStkU8dNLDS6P6sHBcpsFt112V9KCryy9n3Z4hFgxLk+b84xv2G0Feb5DSg8W0Nbm4I2zGIiPLUqv6MnqAsZ8nM9W/1zuHdRw/OlIUhP+w6QAth85AQCgiAAAgAElEQVStbxnc9Z9bc2kyq0Kat7U8r35tnTNvtza3BbyYIqd5fuf7/HzfX+iYi7EWNI/zdQn/q8avItAHGvXuUf95ee/6OBTmyv90KswfNj0jsyJj8am6c7aFoFkHqj78Gpw43XjO+VRVYO37S32oqoaEaNYqKKs2dN+hycIPBHTz4q1Cj71XB9HtWR8SC0zXdiK4nTgTbOb/Wtd47qT8IQEL7Sv1MHfTAZwOJRzEhUfsw9eKZrKnP97PkQqPkOddkUNtEDITo5n76QGyT5xl7JLv8QY0w7Alv8RrNDyJe1RBgrpAI2PCHaP6Myosr6NeBV9AY3/pWQKNIUa//T2zruvB/jIfvkaNEYt3UlAqsvuHV+/GG1BJvKTJPGV/mY/8krPc9MYORizeye7iGuoagxSWi9+PWGhFN+u4Jd+TV3ya5zYXkjVGNNXUBaGwwkNRhZe8Yg+l1XVc/tJXDHtlG/ll4rqB8Ct5QzCE067w7Mf7yTlRzZoHLuPvj16Jw2Zh7vAUiqp8vHBrKlU+P7uLawiEQvgbgox++3tm/qY7o97ayfWvbafkbC3DF37H3hOnmbk2D3eEaPqxmwPTqj1UeuqZu7mQReMy8QVFcTn72GljEWpoFFruN7y+HVXTCDaqBDWV2oCKN6AZjlD6vrqtXvEZH5Pe/4Fb3vgWfyDIrHX7mPL+bq6a9xUPrAyzV4Iqj2/Yz0sj+zRrkGpoDJFfVmMwcBoaQwac0bKLVteuN0MUeudoTV3A6GL1+BvREG8x+nV0EbNz/m4bQ+cE7gsF3+IzvmZwjX687hXb2tCz90nv/cD0tXubQT0t3y5aW7j+me5VM4uotfFLgvW/o4nqVxHodRckgG7tmzIPnVKpZ6MgNG3Mjk4NrTyrSAXqw3oo61ooM55vzLi6MwDxlziIkAUG79eagnBdCBzhDtm6ILwUNhfp3SkS3c60V4cIJElY6gFEWhQaw4uYX4OfqmppOeym7x2CTEKPME2yc7sIPn5wCG/emUmvWBdWq5VL24sjXv3ikLAQDMtC1IbfGFw2mTfG9eW9iYP5x6PDWDv5MlymOkJavIsXb0s3YLLcEg8PXtW1GbyTnuAmM9FtuFXZaCpY9+7owumw4LSK57zkjkwsYUaRpMhogMMiBOoyE9yse2AwcpiC6gqvoserhNSxhHCYEvChRrcOTnyNkF96lre2CR2b/knR/HXGFSR3cjO4a1viYyJZeY94U9XNN4oqffx5hDDs1jSVI1VeMhLcxMdEGoXRuZsL+fShITy1IZ9nP84nygIz/l8P9pTUkNDGzoTl2fTo4MLlUGjvdPDuPQOoDQpTEl2qYOrqPRyp9DDl/d18/9NpHlq1h7zis0xdk0tavItL2zkNJUcdb144PpPPHxnGc7emUlTlRUaif+cYoh0WIxPXM39V05iyajfXvLqNObckkxwXzZFTtbwwMhWLRaZXBxeHTvmYc0syi+/sx59HpJwTcO1WhZRObiP717tvKz31XDt/G5WeeiPA6FRJcyDWce6GYIjCCi+zb+7N7Uuy2fDgkGZOTp76wDl4uL5QTFuT22rwbRngxJy+Ye7w5GafA6SGaZr69taGbjJ+PrEys9yB/nNri8/FBF6HVWlG9fyfjP/f0isjOvXQOtz9Ot3aR3C0qoldYwPi2tk5dqrJ7Ds9wcW+kguL1kdaQdKgZwc3QdRmnaoXO7JGiy7dSEXwtnUdnZZDl0fQoRBdP72xUaNri9pAr47nF1fTceuMBBf5JV6jM1cCBiTGcPiUj9dGZxhQ0ar7BnLnihzjq76v0ya8SN+5dyBTV+1hf+lZVFVos1uAKIdCSsdo9p44Q71I5hmU1IaApnKovIa6ILgdCqFQiG6xLvaFxdIibTKfTBvK7I0FFJTXGE1aiiTgl6Xbf+S5EX0Yt/R7enZ0kxPWXB+YFMPuE9WoiKA98zc9mbAiBzX87GRFwdsQIi3OSdbYTK5/bTsZCW5UTSOv1Mu6BwbT1mlj1Fs7WTgukzf/cYSiKi9L7+rPxJW7yOh8CS/cmorbYaPK5+f617YTZZOpa1QZ1EVIIswblcbjG/Yzb1Qav5n/tfE89PlrYZbT1ulDef6vB1l8Rz+mvL+b/LJqUjpGGwvZ3BEp3JC1nQFd2oAm5AUCgRCBUIiCcp8w4G5hjNHQGGLah7nsL6mmR3snDruF+aPTmbF2LzaLYmjXm+ETnW6oUw/dETZjnypvPcMXfkfPdk72lFYbAms6Vq1j+2iwcHymQcOMjrRRfMZnCKItHJ/ZjH2iBzv9DcEsmlbpqTfw/uhIG5Weeoa+9BUDOrfh7bCptxlDb9kUdSEs/kilh/ZOB9e8+jVfPXY1wDnna9k0pY+fw7bN9Egz20Yf56NwXmhcLIXzl8zzfzL+ozB6fakpO92cQhmAZkEe4KdKHy6bTIQC8W1af3h1jSLw5pZ62F/qMySL9WGG6c1Sx+bxh0/2G41AepDXGT8uE75TG2wK8hKQGucUzj3AwZN1za59KBzkW9YJenRwGA1bj1/fm7TEJk17l8OCLEtcekkkE031AJtFxmmV6Nwu0lhs0hNEg09OcTVVPj/IIEmywTIIAgltHMy8vocBGznt4dcIVaM+7OG6aHw/6hohv8xL38Ro+sS76B7rZM6mA6ho6MlBlEXUQiavzuVASQ3PbzlISlw0L4YLlgAzftODzMRooh0WVFVjyfafyEwUTKfuHd30jHUKr9cyHyer/URa4cdTtYZP7r0rsrkhazuNwRB3rshh14mzxvU9AY1Z1/XgmU8KuHreV8zeVECUTeajKUOMILTkrv7YwwHVblEETTIumrR4J9ufuIrPZw4jLV48tzmfFiBLEh5/gIJyAQvJFolAMMSBkx7cDisDOrdh6V0DWDphAHOHp3C4yseJs37WTBxkFCv17FBv7Hnh1lRS46KxKDLzRwuZA4ssNF90SuKk90XXqsOqNOOUz1ybx6T3f2DamlwaGkO0d0XQJz6aN+/MZMeTYmGZuS7PyNg99QED2zcPf2OIP2w6IDTtw81W5oxcz1if2phv7KMvWnaL0kwnJ9Ydwd8euQKLRTbE0PR71jVtpocdqlpCQ/pcpq4W0NftS7JpCIZIDev8mPF2/a3opZF9Ws2kL4Sb68eav9c1dsyZvv75LwnEvwR2+XfANK2NX0Wgv7SdgG66xLZu5N0nzmV837W9YA7Uh6D0bNPDs9CEletfe3aMoH9iNMsmNNkAAnQzddRWVjcXKjDj7eZuWQibBUkSKZ3crJjQn5bDocCxU7WkJrYhUoHlE/rTJQxLOWSICou29Ypz4bLJRqfu0Qq/wbmfsCKHgyUeA9J5fUwGFkXmcIWH1HiXAesEgiq+Ro3TvgD14YXm8et707ujmygLtHc6WH73QN67byB1oaZu2zm39CHr74cF7AMsv1skA3mlHuyy4MZP+SCHCAu8d+9A1JBKXqmX3BKPYJdYFf77savZOn0o66cOpexsA93aR1Cvwv1XdMEiy7gdVlw2mZQ4Jwv+fphDFR6yxmSQW+Lh4Wu68fq4vjhtEvtKPOwpruF4ZS0pcVFMWJFDXSP0jHWxYKxoBurewUV6gpveHd30TXTTLzGanrEu7l+5C5dNYv4Xh5k/Op2vH7+G2TcnUxtQmbv5AEUVQoSsyiuojXpG3Cc+BlXV2F/q47QvwOyNBfxY5TPsGeePSeepj/PRNFAUmdk3J3P0dB3rp1yG3aIY5522JpcxS7JZM2kwL93ah/HvZFPpqW9OQQxLC8zZXMhLt/XhYIWXmWvzqPLWG4tvdKSNrx672tCYNwefhkaxwDx8TTdDhbKhMcTCcZk8vmG/MBgJXy/WHcHfZlzBrHX7+H3Yf9ZuVUDCsB7Ur6m/RbQMnroAWaw7ohmuP3NdHgvHZRrZsL8xxHNbDhrbzLRJ/TxvjO1rLChmPr7OdV98R79mxiR6EbplsVafj/n8F8Lajf+LpuuaO20d1ubGIhc6x4XOe7ELw78Dpmlt/CoC/bFTIns7fqpOmIK0yMCPlDdBNSqQ0imapLbNM/EgonnJSlMT06GT9RwoqeGuMLRhnK/Cb+D/L45MA5rcn3T9r9ogJLZrroYnQqVGMKTyyNrcZp9ZASWcQc+4tjt1IcHIKQxLHPtVaOcWV7nn8i6AJvToFZq5ZdmtAs9vQMhBTF+zmwev7oqvUcg1l54RbwW1DUEUCS5x2gzDlYdW76GoQjB5PP4AVd567nrn+/BzEzc2blk2+0vOGvo8k97/AVmSyEhwE9Cgdyencf93rcghr8xLeoIbp01mf6mXF25NpSEYYvyybMYu2Um3dlH8GIbbpq/ZTTAonIi8AZVD5T5yikUD0oy1uaQnuBn/zi6mr85FkqTw4ifjC8KBsloyEqNxhh3idYjq0Ekv+4o97C2u4WiFj6KTHqGyGdDo0t5pNBV5/AHGL8vG5bDw/Mg+pMZFM3NdHre8KXjz+it81u0Z2BQFt13h7hW72F96hvqgRp84J0UVHsHRDgld/A0PDuHlzw6x6v6BvLC1CI8/gCRJ2C0K88eks27yYEYv3sHk1bn0iRe4uJ4d6hnyMxsLDNnh9VMuA2D4wu8oKK/hgZU/iK5Rq8LUVXu4at5X3L8yh0pPvREQu14SyZil2eSXnTV4+SDkh6F5NuqOsBkmG7rByLIJA0RgD9/X9VnfGIXa1oKnvtjobwlmbXsQ2awuY2yGZlpbNHSf2YbwHFs6N5kNPM4XCFue/0IsntZGSzaPfs6Wi0lrtMgLLSLmZ/Zz45cG+X9F9v+rCPQqIrjXBQVc0zFG5LqXtrcj0cQCccgi2NU1NnLiTMM557EhAr4MBrxTrwnDb/OwAD+FaYyRYQ0bPa/XM10JcFlsuMPOV1EWHWKSOFThoVdHd9hs22UcX9coCosPrdlDlFUyircgICKdOjlz/X6CQQ0Z8WbyX78Xhd0IBWyKQlLYAP2PN/fGF4Spq3aTmehm09QhpMbFGHo0Azq3we2wGa/py+4eQGpcNP94dBhuhy3sISoRZQU5zF/vmxBNr45N0FD39k4WjOtLpM1K38QYDpb76NFRQCt94oWj16LxmcgSDLy0DXaLwu8Xfke39k4kSeK5W/vgdihEKKBYBDxzOmwOvjSsA5OR4EaWZSJkhQ1TLmNvqQcNiXpVWBtuCmvNzBudjizLPHtzMook5tuowsdThzCoa1u6xzrxNWo8+dveOK0S+0tFdv3SyD78YdMBUuOi2TJ9KC9sLWLxHf3IGpNBr1gXh6t8RgB7fMN+ZFmiR6yT3h3dZCRdwuZpQzlxpp7agMqja/M4eNIbDuiiKevO5TnMHZ7MHzYdYPO0ywG4ccG3zN5YgM1mZdV9A3nv3sFMW5N7jlF1QblYPCa99wNjlmSTNTaDrx+7mg0PDuGH4rPc8Pp2PPUBZEUiuaMbJCFDoGp6k5KQq05PaovNohjwQ2GFlypvfTOzar2QaW8R1HQfV1XVjIXKLA1shlKunb/N0MQ38/N1RpC+vSXs0zJY6oXWI5Uerg3b+JkDLJwrC3y+4Go+vy5rbL72P0N7bLlfa9supJH/c5DMPxus/1VQz6+iGNsmqbeWcE8WAN4WrfRpcU4sioKqCgghNS4Km6w0kwe2A4pVEsFBklg7eQizP8nncKWXbrEu/MEgheW1WAGrVWqVzhgRbulfMaE/M9bmoqoqn04XOjh3vfM9jUGtSYveKiHLMqFQCKvFQsc2dg6W15KZ6MamKDx8bXfuXJFjNHnpX/WCa8+OkRytqAtrmO9vVrDVm60k4LJL2/LQNd3I+vshik7WkJbYFlTIL6tm6yPDeGZjAfPHpPPgB7s5WF5DSlwMhyq9bJl+haGRM35ZNvExdgrKa4mQ4YvHrsLtsHHnOzspKPMRGXZxemztPl4dm86ja/OYOyKVW97cQSj8mLZOH8q4pd+zZfoVuB02PP4Az3xSgL8hiEWWDYXIdQ8MZuJ7OUiSxCu3pfP4+r0kx8dQWFZDt/ZOfjpdx5pJg7njnV2sn3IZT67fx+vj+uL1B3nuL4W8fVd/Hnj/B2wWhYev7cb8Lw4ZGjV6Me3JG3vy/JaDBs+9vSuCSe/9YOi+gHibcTuEOUgwqKKi8d79gw1pBI8/wNXztjGgcxveHJ8ppIHfyyEQCGGzCpMQWZKQJYmscEe23SKkdS1KCz31cGPSvFFpho5PSw0ZHes2F1099YL373bYzpERhib9mqvmfcW2x685J3ibC6QtnaRammLrOLfef9CabrtZQ0a/vn4tf2PIePMzu061hm2bt+m6Ny11aX6uwUg/f2tzbKkweTFNUv/M0Fk6F5JTOB+2f7GNW+d7dr+kZvAfVYytawwBGgmXNP8DjLBK/FTl4/BJwXkGOFZZ24wHDiKI1jVqdI91GYW6vSUiuMgSBnzSyLmGtmnx4Zb+8M/TP9xDKCQagEa8uZ0Ri3dS1yiCvN7a72vUSGzroDYIL9+WxsHw+SVJorDcY7Bg9HcO/evLYUpm2el60hPcvPy3g+d07Npkhf5JMQzq0gZV05i2Jpfc4hp8jYION3t4Mj7TYvjQB3vYXVxDbRCKKjyoqsbvFnzL4Be+ZOyS7+nWLoqDJ8X86lV47KN9lFbXUVDmI8IqUR+ER9bsJfvEWWas3cue4mrmfnqAfkkxRpF39sZ8JEnmsbX7uPrVr3nmkwLm3JLMoUovhSc9qIgeA5tFRpYVQiGVqatz8QQ0nv5tbzQNfjpdR4/2Tu54Zxebp12O3SKzt6SGm17/hpve2EFBWQ0ef4BDlT7qGhoZ/042heU1uB3hgmD41b+d087OH0/z1MZ8IepWHzCy5qmr9nDlK19yzavbmPK+gJFUTaMonP1e8+rXTF29B7fDxoDObZCRDC/XxeP74bBZeOm2PlhkmVdGp3HgpIeZ6/KYtW4f09bkGvK+epFv5ro8GoIhAo1CrE1FmHbowaqmTmjI6PLAekC+e3k2l7/4JU99nG8oPYLA0nU6pD6ksMqnvgjo2Z7dohj0SH3oUIoZE9d55u4IG9PX7jU492ac2wyNNASbF4dBBF0djzc3HLXGRTdv0xe41uiPFwqC58tooyNt/C1soG6e278LA78Q3PRzQf5Cz+5fcS+/ikAfZRNsiLm3iCxN7yB98oae+BqFXnnxmXoiLaI4F2m14pCb+OZRNhmnFZCEXvvsT/JJTxC2eIcrfIYcMCBayk3DIsk8N6KPwZwJhbSw2bebVZOGALDgdpHVKbIQuwJRT0iLd7JixzHcdoUld2QSZbPSq4OLtHhxvdQ4JwvHZgjpXqvE6uwTAHTt4ORIhRckCQkMWeaFYzNwWC3YFJlXx6QjI7HyvoFkJkbjskm8OiaduZsPGIuVzq9Oi3cS7VB4/fZM4bka56ZfUjSapvLy6HTDUDxSAWS4Y1k2yXFRWC0KH04azOvj+zIwKYYou5X37h2Izarw2u0ZpCe1ZWDnGGwWhTWTBuFwWPj8kWEsHJfJ3M2FpMRFs+aBwewr8SIBd6/YxYYHLyOjc1t6d4pCCbtq1QZUNjw4hNfGZhgc7z9sOsCAzm34ZNoVZCS42PjQ5SS2dfL5I8NwWC1snjaU5I7RRvejLk8rfFUtPHtTb25c8C0z1+bx6UNDsFtEB2jvjm4+e2SYMNTWhEdsr1gXdovCp9MuN1g1akjDYpUNSV+7VQTOJ9bvDzNsbHz+yDCW3z1QaMCMz2TZhAHEuiMM2GPu8GSGL/yOwnIPL4xMZfH4fgaMpgfPmb/p3owB88bYvtgsCpsfHmoIsJnFuJbdPcCARexWha/DlMNrXv0aT32gmYyCeZgzTzMmbvaLNbsrmSWEAeO8T23M57NHhhmceX0hMC8++vhnJHv17Q2mhebnzqsPfeH0tzj2YjDz8zVHXQj2+bls/kLjl0JK/+oF61cR6EGisVE1fFAXjeuHDfjTliJS4pykxjnxBYQ+el6pF099A34VDleKbDjpkgh8jRAIiWh9oLSGo+EW+pCqcrSsiUffsvM2t8TDj5XNP0+Nc3K0wovTYcFlk1i2/UfRFVteS7dYp2DcaIK5kV9yls7tIpm8OpfJV15K0UkPP1WKDPrHCh/T1uYxflAivkbBCwc4XObFG9AIBkM4ZHj200IAnlifRyAU4kB5DY+uzWPnsTOMWLyTQxVi//JqPwWlZ0mLd/HEhv38bsF2Cspq2F/q4xK3VRQwNZh1XU9sFgVvQOPZjfnUBUQFQlXh1dHpvHvvQArKakm6JJLbl2Zz42vbUDWNycMu5c4VOfgbgzyxfj9qUGPG/+tBYYWHsUt28ucRKcS6I6jy+UUxUIN2Tjt9E93YZehySSTtnQ4ev74nBeW19O4YRTunHaddxucPcsubO6hraOTxDft58saeyJLEsx/nk1fiZeSiHdTUBfD4G9lfepY5mwrIPn6Wg+U1TF21h+nXduO+Fbv4bdY2erR38uLWIlbcLYLWM58UGEqVB096mL2pgJte/4YfTpzl8Rt6Ulhew3WvbeOpj/MJBEP8bsG35BRX8/Rve/Hw6lyunvcVP1Z5BS6uweZpl+PxB4SWDsJ1aeqqPQbOr7NR5mwuZPXEQXRrH8UT64QkgR6kHVaFZ24SSp9P3tjTaLjSBbnGL/2ekrO1TFuTiz+sJKrj6Xoh1Iy59+roYta6fVR56w02zgvhN0R9P2iCN4rPiL9p3ZZQx9j1RUAP7GbBMh2X1gu5Onxh9gmGJlijtdESt9f3N39u9qg11xhaLgItr6n78JrnoC9E+rGtda6aFx3zfubrn+9ezsfGuVixsl/SSfuvfCv5VWD0MUm9tZjx8+nVIYqiiloRGFqxlEqLd3L8VD2qqhoNL+Yh0yTv27tjlGHqrQ9dbtdxHpwexNuBRZap8QfpnxDN7rBImu4kZRZL04fLrlDbEMIVFhWLsiv4WmvZNV2jNqAaloWRpvlkJrhx2CyoIY3dJ84S1ATvXlex1K89sHMMhytr6dE+isIKL76GEFmj01izq5ic49VGI5VuL6iPAZ1F5j7zN915+bMisn8808z5SUZID49ftguvv5GQJpyzDp2sY3CXtswdkcJvXtuOyybkjnt1cLH7uJgnCIvDo6drqQ5zPlfdN5B7VuYQUjGey7oHBjNuWTaRVonl9wxizNJs3HaFjQ9dzg1Z2wlp4jnklniEWiVN1o76M5IlibxSDwM7t2HBuL7M/CiPgrIaQGLNpEHc8c4uul4Sic2qkP3jGQZ2EXj8zLV5BDWVg+VePp46hBtf/5a0eBf7Sjw47aLVuGcHF7uPC63+zdOGcsubO0CDQZeKBizd6GPu8GRuXvAtNQ0hFAlcdgtpCTGGDZ+/McTdy7OxWRVkSTIatwKBEIUVosbUo52T3NIavntKND5dO38bmx4aYgio6Vo2s9YLa8LdJ87y1WNX8dTH+VgU2XCS0qmWAEcqPdy+JJu/zbjCwPE9/gDPbCygoNzDV2Hrvskf7EZVtWbzbSk6pmPz5g7Ui8GvoUkSGJq/cZiPB/EGomPvwAUbpMwa+3pdQpc1/jnXKX2beV7n66zVj/mfiJX9nCTy/8a4WIweTdP+7f8i4npo/11Qro1fslMb/uY3WkHpWe3WRdu13OOntZsWfKWNXLhd+2xfqTZm0Q5t7JLvtNzjp7WuT23R/pJbrHV+cou2vahC215UoR2uqNH6zP6rlnv8tJY+52/a7xdu1/rM3qqlzflMu2nBV9rYJd9p2UertJRnt2gjFm7X/pJbrI1c9G14/8+03y74Wjtx2qtV1zZohytqtBOnvVrnJ7doN7/xtTZi4Xat85Pimhl/+lwbsVDMs6D0rDZm8Q4t9/hpraKmTjtx2quNX7pTuy7rS+3SJ7doqX/YomUfrdJGLtyubS+q0NL+uFU7cdqrFZSe1Q5X1Gi5x09roxZ9q1365BbtpgVfa31m/1U7cdqrnTjt1Spq6rTso1Vaxp8+N+4r7Y9/0/rM/qtWUVOnVdTUafWBoHbitFe75c1t2p3LvjfmXR8IaqMWfatV1NRpG384rnV+cou28YfjWnVtg1Zd26BNWJ6tHa6o0ca8tUPLPlql9Zm9Vcs+WqWl/XGrsc/hihptxMJvtIw/faYVlJ7V6gNBrT4Q1Ma8tcOYY3Vtg1ZRU6f9d0G5driixpjXyIXbtT5/3Kpl/OkzY07jl+40ntOYt3doaX/cKvZdtN2431GLvtUy/vS5duK0Vxu5UGw3zzP3+GltzKIdWt+5n2uHK2q06toGTdM0rT4QNOZy77u7tBOnvdqdy74X13prh/GsJizPNuauaZpxD+Z70Z+N+fmOX7bT+Fy/Xn0gqI1fulMb8/YO4/j6QFAzD30/fZ76fvq/+kDQOKemacY2/Tz670rfv+X1q2sbtMz/+kK7c9n3xrZ7393VbL/q2gat3399YVy75dxazre1+bccrW1r+fm97+5q9Zm0drx5XtW1Ddq97+4659r6OfVtLb9ezLzM+13Mvhd7vvMdO2F59v/oHD83gB+0i4ixvwroxmGReevro3j8DeSVeLht4Q4aGoPcuex7CspqKSyvYfLqXHadEMJaz28pJD3ebVAjX/6skMnv5XDiVJ3hqSpJEpqq0ichhsXjMykoq+WeIZ25Z3k2tUE4WFbDE+vzUFWVQFCle/soik/V8thH+wCYs+kA3vAr9U+VtaiqSnqCi2lr81g4ri+HT3p4esM+nv54HznHzzJi8U4mv/cDDUGVB6/qyqGTdXTvEImvEZ7fWsju4hqmfJBDKCTy5+f/epDfv7Fd8Nwlwfo5VunDGxAF2GGvbKOsup6JK3ex8t4BSJKEzSLTI1bQGnW3pobGEI+t3cfRk158/gA3vLYdEFnSoUovk9//wejsnbl+PxNX5uDxBwg0hhi5aAcHT3qZ91kRdY0qL/6tEE+DiscfoOikhzmbDiAj0a1dFHM+KTBe4WUknli/n5vf+JYp7+/m2Kla7nt/N/g4LI0AACAASURBVCdO1XFD1jfGPj1jXfTq0NTsFmgMMeGd77nutW3Muq4nkiRxytfAoZMepq/O5brXtgGwZpJQqNxdXIPXH2Tu5kJUVWPieznM2ZRPQflZVk8cRCCoGhCAjmnrzTV6sfKUr4H8krPMWi9+r38ekcLNb3zL8Dd3UHzGx1Mb8pm6ag9PrBPPyB6mMD635aDBtAFYfrdoursh6xvjddxhVZj7+xSW3jWA9q6IZgqS+tBhiJnr8th74gxXvrKNiStzsFubjEH0oqVeDNWvqStyqqpGQ7Cp09N8bl1JUodxAKP4qs8xOtLGpoeG8IdNB5oVb3UYxwxBmJu2zPfZsoDbcpyPEtnaM2ktGzZn1XrjFDQvzLasAfxS+KNlJ+yFoJnzne+X0B5/Duf/vxy/CujmfA5TusG2rksfaRWv8b6fcd2OVCCxvdCVSYtzcuxULYFG7bxuUNAE+4CgWE58fzeZYejGaZPxBVRjPi2Nwnt2iORQRRN7pjV4x3xMalhCWYeWdKtEhwQWi4QkCaGyKItoXErr5CS/3GcYbEtAvzBEsmbSYH73xg40BO1TkqBv0iXMH5POda9to8YfOmcedptCz1gXRRUeusU6sUgyATXEj+GFJrlTFIXltefcZ4zDwl+mCyijRzsnh6q8aKqGN6Aa9xupwJrJQxixeCcywkRFr4vIhCUXAG9DCIcCNotCTRjmilREw1pIFQylJzYW4HJYSOngoqjCS7XJFUt/NhnxLtwRdqNx6qWRfZi5No99JWfQNKht1NAQEFLPji5mfLiXA+U1fDz1cuZsOkBBWQ09Yp3sKa6mX2IMR07V8vkjw5i5No/l9w4Ms3W28d1T1wJw3Wvb+PujVxl1hpve2CEK1orC8nubOrB1CuQNWd+w/sEh2C0yV72yDTX8HL54VNg36gbf80alMfOjPA6U1bD54aHYLQo3vL4dTRM+rg6bhSdv7Mldy3PY9NAQnvmkgILSGj6ddjmPrd3H7pJqBnZpg0WWCQZVLBaZZXcPMGCdSe/9QDCk8sroNIOuquu6625SnvoA12d9Q2qnaLLGZnDj69tJjY82dPd11pGuhaNr6LSEcloGYX+LBcVM/9SfVWujNaqlDt/o8A5wQQ0b83FmLR99/5YQlL7f+aCj1szB/52B/D+KXtktLBPQQnGAV0aLrlVHWDqgvlEUFJPDgfKhYUnN9tclaOpCUHSyjggFfqoSwetCQR7grTuaeNj3vb+bCAs8eZNwQVpxz0CcdsVoqtJxY91BqfR0HRGyYM3ICMZOpKlZyqGIgBlu+qSgvJaC8lq6thfduUcrBIbu10RxbPk9g5CAnuG2/xNn/URZJeaHnwfA4SofvTq6eHFrkaFx07NTNIqiMGd4MnaLQp+EGPonRtPdpP7pV4VaqEWW6dbeSV6xh5wT1eSXennrzgFCwEyxoEhi3yirRP/EaPonRfPFo1fS3hUhdFssMilx0aydMoSocFuxrnL53BahU26XQVFEfcNlk9k4dQhpCTH8dcYVZCbFUB+CV0al47LJRCpgs1lZM3EwLrvC058UEGGBT6YO4b2Jg1nzwGCD7pncKZI+CW1Ydd9Ajp+pJ6iqhhvTrHX7wlLAGnWNGn3DXb3TPtzL9VnfUFBWTUpctPBnlaBXBxeL7uxH34RobDah/AiiIclTH6C9K4KBXdoCQqZXpzuqmsaLW4vonxRNMKQKobf6AJPe+8HQeNEbn258fTsNQZWBXdoIM/oIC0+v30/2sTNU+fyomqBlAvTu5OaZjQU8vmE/66dcxl8eHorDZuHPI1K4a3kOH00ezJzNhSwcl8ln4V6KQ6d8bJ52Oe/eO4j5Y9I5WOlFRaPKW8/Ql78yJBcKyqq5et42oxv3bzOuMFhE+v2pqrAodEc0STMARpDXtXDM2jfm0bLwaS5KtmT4tNScae08LbdN/mA3U1eJgK3TUVVNw1PfZEJ+vm7WrDEZYpG6QHKr73c+bZ1fqnqp7/fvHr+KjD4mqZcWM/414+dXwtmcnhmve2AwL/7tIHnFNahAtF00fujJqk0Cm1WhR2wU/lCQwxV12GWwWETzi64jYwcsNgnV5PakZ/LmgqcEOO0yPdq72F1Sw9bpQ5mzqYCcE9U4ZLBZZS5tH0V+iRckjMaiKAtYrRZW3juQEYt3AtClvZ3jVYJJ77TLeBtUkjs6KTlTR12jSkonJ8dP1+NpEF6oigQZCdHsLa4h0iaT0jGaIBp7TlQjh5UW35nQn3d3Hmf+aOENOnXVHvaVnGHrI1fi9Qe5Y1k2KXHRzP19CqPf/p5QMGR4ui65I5NVu4oJNqocOFlD13ZR/Hiqjp4dXViQKKzw8sXMYXj8jYxc9B2+hhADO7fBZlMMWWCdQYIGsiLhbwhyMFwQ7t85hidu6GVo20dZZWobVaIdFlI6upEVyaAV3rtyFz9W+tCQ8PiD/HX6UF7+7BDzRqVRVl3PyMU7Gdy1LQvG9hWa7iGVrrFObIrMwZMe+sTHACJwePwBntqQj6xIzLklmTmbD/DsTb2Jj4lkyge7WTBOSO8+sX4/b4dlge9dsYuDFV5WTxzE7xd9x5ezrjQckyo99UZn6LQPcw0dHKCZH+rEd3PIKa5mYFIM79wzkGlrckGi2bPy+AP8YdMBgkHVmN9zWw7y5I09efWLw0Zm2lLB8sYF35LayW00YZmdlKAJUqny1hvGKLq6pF6c1ZuWis/4GL7wO9ZPuYyENlGt+qvqDlfzRzcZoOv76UVL/dp6k5q5QNuS5tgyc28te9bvo7XRWrZc6annxgXfGgVl/RnrTWF6tn6xbwkXevu4mKz95zL6f1VDlz7+ozL6QFDFIfSXsAEf5ZQAQhTMbZeZ97ciLJJEhAWiHRYW3dGPQKgpg+7VyUm32Cj2lngoLK9jzcTBpCe2ZdmEAVgUmUirhF0ONy6pGuZ+q3cm9Mdlkw2Nm8ykaNY+MJjkjtGoktg4d/MB5o5IJTPRjV8VWbdDETDGxw8OISPBRUpcFOmJbdnw4BBe3HrQOP+xqgYiFHDaJN66Q7wuFp+uZfk9AxnQuQ2RFis9wjh238RoNj88FIfVQkq8wOIDagibIjOoSxv+8vBQMhLcTF69h6d/28twGXppVB+8AY1H1uxl/LJsuraPAk1osL97zwA2ThuKyyaTGudkTU4JC8dlsmB8XzRNI9Ju5e+PXslrY9Kx2RT+8vDluCNstHc6SO7opn9SDK/eni40bhpD3P9uDg3BkKBG3tybObckh/F4J5d1bUvW7RlkfXFYSEjYRYb8j0eHsWX6UCwW2eCUNwRD2GWFLx69io+nDiE6woLLYSEYVIWW+j+OkJkUjUWWsVsUPn9kGOlJbXj+1j6gQZ/4GLJuz2D5PQPx+AMMX/gd+WWCMjnm7e+ZOzyFO9/Zhccf4EC5YE49s7GAgxWC4trQGMKiyKyeOIiXPzvE+smXkdjWafwnN/PlF47LNKQJZq5tUle0WxUcDguDu7ThtbEiyC67e4AR5HV8vL1LuE0tv3cgL9yaygtbi/jziBQjyNutioG/6xRLd4SNr2ZdZXjH+k1sE2iukKkzdGauy6OmLtDM0EQP2HM2F/L5I8PoHuvGEWYOtQyI+r3qTV9mLr7eNavvb+4X0EfLIKpLCOj/zPCKGSdvOS60AMS6I/gq3BmrH6+LqumuXhcKqC2v2TLIt2xa+rms/eeC97+zocs8fhWBXkM4Ks25uRcBYF+x+I85Y20uwUaVnOJqDlZ4qA2KP56H1uzB5VB4d8cxbLJwMcor8fCaCdooPOllzNJsktpGUN+oobM1gyrnuEyFQqoB3QRCKmOXZpNfepYfq2rpE+8kGFK5+Y0dBMLOQZIkUVBWzehFwjUpr8TLgbJafA0Bnt6wn4MnRTDRTb/rQhhFYhCdtbcvzRZOSxU1vDwqTahZKjJzPz2AvzHI/lIfITVEbokHf7ggN3tjPkcqvSy5ox/PbS6kzt/IDVnfGNoyR07V0rltBIcrvLw0qg91gUZGLN7JzA9z8QVUCsp8zPxNd2Me3gaVl27rQ0MwxIhFgu/9zMYC7l2+iwnvfE/hSQ8FpdXc/Ma3DHtlG/et3EX2sdPMWLMXfyDITW/s4Ias7WSfOMvu4hoeurobz2wsQEXD7bDw1h39KKrwYLfIPLUhn/lj0lk4LpMfq7ziuGNnaAgKJcRu7aJ4YsN+9pWIbQ9d3ZXDFV7mDE9myvu7aQiGePq3vbjpjR3kHBf8eF0JcsSinayfchm9O7p5bnMhyZ3ctHc66NXBRUNQNaiuy+4ewOePDAMErlvX0MjYt7/D52/ktrd2cqTSY0AMZv5zdKSN+aPTmT8mncIKbzOe97IJA5gzPIURi3Ya6pX6G4/ODdez4ipvPcPf3MFj1/fgD5sOGMYjgJHVF5/xcflLX3Lvu7uMgru/IWgUSc2aM2asWi9CmxUgzcFJV4E0d8Kah/l+88tqqPIKoTpPfYBpa3KbGZfoC8X0tXvPcaPSOfF6V27LAKk/lwspRpr58S0/038fLbeb5Y3Pd1+tncs8/lWNTP/uIA+/EugmIq6H1mHC66TFOTlc4UOWBa/80nY2wzJw4dgMnvkk33AjWjC2HxPf3825bHuIdigsGt+PO1fk4LJJhFSNuiB07xDBkbClnq4/o48+cS7yy7xEWaBLe8HBXzFBGH9HKsKEvAHoEevgseuSeXB1rnHtCAUDCoIm05IoC/z51jQeXb8fjSZueJQFkto1mZKse2Aw96/cRdf2TvaXekkNF19fC5/HbVcMow99KAjHq5S4GGRJIvv4WaOILMvw2SPDuG3xd8S1cRgSECAwd6tFtLRPWJHDlulDuWt5DvNuS+PR9Xn0aO/k4EkP3oBq9A5kJrg5WuUjtVMMr4xJM2QJqnx+fP6gAVMB9E+MRpYkgqpKXokHFVEw3VfqZUBSDGhCL3/T1CHM3VzAkgkDOOVr4OY3dpCeKCCrtE5O9peLukVqnJOCMp9RyPU0hFgxoT+z1u+jur6Ry7tewrM398blsHDzG98C8PdHr8JuURj28n+TFt+GQDDEO/eIQum0NbmoqsbcESlc/9p2NCA9zoXFonC00ktqQgzBgIrNrqCGNJbfOxBPfYDfLviWL2ddhccfILGt08j0nrmpFzdkbecvDw/l1S8ON+O+6xo4M9fmkTU2gxuyviGkaciSRI92YROScMYeDKmGmcnM33Rn/heHQcLgzg++9BIW39GPqav2GFo8NpvSLNvWOeQgArTOodeLvXaL0mrhUrfu++qxq7FbFe5ens3hSh+fTruc4Qu/IxQK8Y9ZVwM0g0b0YNnQGDLMwt0OWzNtHbspg9aNQMzXhnNhHx0WMnPizweBXAhqMRecdX2flrDSxQbhi4Fo/rcC+i85138UdKPXYPeX+QTuHu7W++lUgF4dI0jtFMW0teKX7g37bU58fzcJbZtW9s6XWI3vu7aL4qHVe0RDUkgzJHn1IG8DLOHKr7Czk3j4GpHp1gaF3EL/pGiSwh6vdaGmReFwpZ/Jq3Oxy03ywmpIQDP9kqLJTHAzZ3OBca6Z6/cjSyKYR9qEdENtUJiS6Ne/fWk23oBGfqmXCCsUlPvQaDI8ef12IaIVZREuVQALxmZQGxQY+ZzfpyADb319lMzEaD57ZBhuhxVN0ygKB3m9cCzLMpdeEsmdK3KIssk8v+Ugy+8WC1oopGKzKHwwcTCKBCvvE1/nDE9FkiRki4TbITRTHlj5A89tOcil7ZxkmoxSAHafqOZIhZf+STGsmNCfY6frUCQ4VOkjqAm8HkRX8m/mb2Pc0u+JsslIaLjtCg6bxSi8Hiz3seq+gQzu2pZ3wtr5y3ccY/2Dl+G0STx+Y09ufmMHv1vwLZqmkdIpWjgyBUPUNWo8fmNPDlV6mbpqD9M+zMUfCFJQXoPdIuNyKETZZPLKvKghleS4aF4a2YeiKi9P/7YXB0568NSLourfZlwBwPA3dxgm2VljMpiz6QBOu4X4mMhmQd4dYUPVNGauzePASdEc9fnMK8lIbMOGB4dw+JSPoKoyc20e80alGfWOP49I4f73dvPCyFQWjstk6YQBfPXYVSy/ZyB2q0IwqLLr+FkKK7z8eUSKoZWjB1cdXrvh9e34A+IPPxhSueH17UwJN0gBzd467FbR+KYbmTtsFj6dJiQpNk+7HFmWmbV+n2HLCDRTozSbhU9fu9dY5KZ9mNssy9eNUcxF1tagEbOOvTngtey0bQ1qaWmCorOKzIVh/Xf3S4L8+YrG57sH82e/ZFxsgfeXjl9FoAeJKIugB7rtCkvDnOUesY7/j70zj46iSt//p6o7nU5IdwKSsAVQRMhKCBAQEJf5OjMuiIiAEFBERZBVRARRhuGL4oKIrLIKKNsgKiIiOo6KLGEPCVkIi7IlEAIk6c7a6e76/XH7FpUmAfSrM845v3sOp5Pq6qpbFfq9bz3v8z4POefLOVlQij1QpV6ICOyJkXZim9g4ffnKY9mpS1XCv1UR1n9xTcNo1yyM+KZ1CbWaq/nOupA+pWALVGkZEcJzq1N5u1csJkXMx2oxE+izkIttXAdboEmoVvpWJZcGb/m07CsBNDCh8NPF6iJlJgU+ea4zbZrWZcy9LanwCtPwEN8iUSdAIaGpHRUhxxvTKEynIEY1DMGkwNzvjwHQIiKE/GIX0Y3q0LHFTXS6uS5z+yfyxpYc6lgUjl5wkpZbzOTPBGvjyzHd6NSiHglNbLh8/29iGtqZ0TfBZwwSimpShNfpzXX5x7DOzOqXwKxvj+tyxh2a12XG10eJaxLGgmSBWU/uHsX+M4VM7h4lTCb6Jwo2SaCJ9/q3pX3zMMo9Gl5NLMitImxsHduNtc92IsPXkXpL/RDsgSaiG9mJamCnVUM7h3MdLB3UgeDAAFY/2xmzCm2a2Hn/h58AeHqFEIt75UHBhnK6NF7fcgSb1UR0YzuxjcOY3V9IEETYg/j+xTuZ+c9jKIrCm73jmNmnDccvlhLdyI7dauGfL9xFQtO6bBzemZOF5agouguVcMsSC1hGXrH+99QQrk9DPzqgm4DHRQo/AGnWLTHyJU90YNngJL5+vptuFLLkiQ5E1q1DfGQY03rGku2rGZjNwn2qab0QvvIFzZFrUhm5NpVXN2bp5zdbVEKDzKwZ0pFXN2bpsJBklIxck4pqUvh42O0c87l0LXtSzMESYNJVPo3OTY5yFzkXSihwlnP/7B281jOGKZuyqajy0LReCNvG36Mbg0jTEn/zbomPL3q8vf6z1MKXi4E0GanJXMT4u1E8Darr4xiZOjWxYPx19o3nk/vL6/+tgmlt8M6vCdq/F6b/h4Bughu30iKemEWwCaIbh/Je/7YMX30AiyqaZkICFKIbhTJ/YDvyisqxmFX6LUphRu8Ehq1O1TncdovKrQ1CsJrMvN03XrdOu1hSyYNzdnJLRCA/XagU5hZejdIqjYUDEhm3PpWSKpFdt4m0M7NvAoFmlUq3l3vf3Y49UOW2CBtmVeXlB6N4Y8sRXn4gSocsFg1IpOlNwUz7IhtVVZj6cAw9Zm/HbDYxtUcM6/flAuD2etl3uoiQALg1wsaJCyWUVGl0urkubo8QIHt0wS5WPtWRgUtSUFUTzW8KYtmTHckrKmfG1qMUV7rIyHXSvlkoVrOZBQPbkVtUxmMLd/HZSJF1hodYq8nhjlqTSmZeMSUuL+uf7cTsfx0nI7eIT4Z3IbKuoKoOWrqHfacKSWhi5/VH4+kxbydbnxdcb6+mMbt/Wx3jHfLhftLPFBHXWPCt/zLrR6qq3AQEBPDFqC5CVbJCkFEfXZBCi5uCCbYGoCoK03vFEm4LwlHuYszaQ7zdJ54X/5GO2aTySvcoBi7dy/phtzNlYxZj7m3J7G+Po5oUpj8ipJOLyt0kNRNCay6Ph6WDkkRh12zS2THzkhOrMTJyi8p4a+tRXS4AqAavyOAFV+SBja+DP9iL2aQyLzmRZ1bs02Gg0esO4fVq+jUN/egAM3rHV5MjAB+bZU0qSwZ1qHZsGWylL6vks/vDIkaDDynsNn7DYV7rGaOzhODqRp+a5IFrgjck9FPTa02josrD0yv2sezJpF9cTP0lo6b53ihT57eGWn4tNPN78+xvFLr5QwT6us2itNDkmYB4xKhjUXG6vNh92jEhFhXVpzVfXOHRKZG2AAUNwYKp9EKQWWiIl7i8urbJuPXpuN1eUk5e1s+3aEAiL31ymKoqN2UeEeAl773SK5ubFBKbhHK0wEnLCBvH8h0+L0GIj6zL1IeF5ouK2KwCIVYT/3zhLk5eLKXv4j3VzM5Dg8y4q9y6SqZZgUCzQmmVxqqnknj8g33YA4VRdvvmYaSeLiLI4vu9WRhmhBPHnlOF1LEotImsK7Bwj5e9JwtFDaCJnZOF5cQZmlzcHi9Z54ppXr8O6WcddJJNNZqXo+ed/ODTOh+8fC/ppy9T6oakpmGoJgUVBS8aR/NLiGsSWk0TRWLAM3rHM2LVQfadLmL9s5149qMDgILH48akmrilfjCpZx0kNQvTjbHHfZzO4bNFaBpENbSx72QhSTfXZfETHRi++iBeTWP3T5cxqZB0cz0WDhR2dccvOPjrrO3YAs1EN7LrWbAxWI9cU935q8Ll5sDpIjrdXI+FT7TXNU5k44+RNmikHAK6lorkar/yYBQPztnJP18Q7JWKKg9nC0vp834KP4y/h0q3R8fy/XVh3G6vaELyUSblYmTEnyUDp6bmHalNI+c+9h9pZJ936gyUatdcQ3CpbVttuPe1MGx5TTUxXCTGrqrKr+oKNQbqa2Hy1zvG70lpvNF5/DvGb4rRK4oSpijKBkVRjiiKkq0oSmdFUeopivJPRVGO+V7r+vZVFEWZoyjKcUVR0hVFaXe945tUhVVPJRHfxEZwgMJHz3QiJEBhQXIiCmA2qXw6oiutImwEm4ToVvtmobRsYKOkSsgKrxychMmkclsDG+ue7cTRCyVcLKnE69V4u288cT7p4BbhQSzd/jONwyyUeUTQtwWqVLghqlEoG4Z1JrZJXTaN7II10Mwnw7tgUVVaNxDuP4qiMP6vrQgPsbJxeGfim9ho1zSUds3DiGpgx1FRxWM+Dnl+UQVBJrAHqqwcnMTWF+4iJEDg9ZtGdaVN07rYA03UC7GgKrB26O1sHt2V+QPasXVsN7aMuYP1z3bCrCrsOV3Iyw9GiT+YV+Pt3vHMeiwBq8XMMp9/7bELTj4edjtLnuhAYIAJl0uwdVpGhHDqYimJkXYW++CEuf0TiWpo1zNfi9nE1hfuYuPwzsx/vB1zkxN14a2vx3arZuwBoqHmtZ4xTPw0g/kD2/HtC91o07Qu8ZFhbHjudlRVyDXMSU6kc4t6zB/YjumPxDJufTrz+ieyZkgn2jQN4/VecSQ2FcbZALMeS2DhwPb8+NJdfP/iXXqQr6jy0DLCzifPdWbbS/ew8PH2THkoWhQy3V49I5/SI5p5yYlM6RHNyw+05vjFUupYVN7uG697mUprPYlXSyaJpBJKlUep9y6v/a2tR/lseGemb8kRSpvlLh5dsAu3R8hG2IMEJuxfcNQNuc0mto65Qy80GvdZMKCdbhoi8WRrgIk3e8Xpi9ecfm2Z068t4zccZtZjCUKe2ZDlys/5s1auxWSpiaFTEyTiP4zmK/5DVZVqDlQ1jZoYNf6Ye01B/lpYeW3z/61GTbWB/5ZxQxm9oigrge2api1VFMUCBAOTgMuapr2pKMpEoK6maRMURXkAGAU8AHQCZmua1ulax49LSNRK7n9Nz9RjG9XRHZHMZpVlTybhcnsZ+uE+St2CgfPKxgwqKz1UaKK4GtvEjlvzcjivhGAT3BIhjhFqNRFZ13qVkqUcQSq0amTDC5w458SrCc/ZuEYhBFsCqHC7OZ7vpGUDGz8VlPBOn7YMXZ1KHbPo0i33itrCLeF1OHWpjFvrh3DUFyDL3OJJocotLA7n9Etg5Lo0FMBuNbHyqY68vjkbs0nF5fbgcnvIPFdCoCoawLxeLyjQIsJG2lkHG4d3JnlRCmUeX9atKLzeO46ec7ZT4hardvumYbzRJ55As0q3t7cRbAKTSeHm8BBOFpSw5fk7qXR7Gb8+jRMFJawb2hmbrzj64rp0ss4XYTKZWDE4idc3Z5OVV8THw7vy+uYjqKrCzL5tGLdeKCkeOV/MJyO6MvnTTLxejcVPdtBhiWdW7OPI+WJiGoUx5eEYpm3KJv3sZcxmwYh64oN9rHu2E0M+3E9xhUeodprNZOQVEtekrm4aYrWYmdUvgbHr0hhzb0seW7yHz4Z35q0tOew5eZnNo7vyxpYc3F4vGWcLcbo0EhrbSMtzYlJEAjBizUHiGocxq18C49anM6VHNH9+dztxTUKwKCbS8orZ+nw3IuvW4enl+8g8V8xt4SFYLWbmJSfqNEejzIJkyqSfKaTE5SXEohDTKKwaG0YGfJnRuz1ezCZVd62SEI8RjpDFUfk0MXzVQdLOXOK78X/S5zB2XRpuj5cj+Q5iGobq5xu97pD+BGJkrYBocJIMn5qcqIxt/8b5GFkw8nqGrNwPCrrj1I08Pfi/7y+ZYJzLtZqZamq6+newXfyfEv7bMvrrBnpFUexAGtBCM+ysKEoOcLemaecURWkE/KBpWmtFURb5fl7rv19t55D0Sv3YXNGKkZK+IAKZxSQ491bf6/VGq4bBHDW4ODW9KYAzl64jliPnpV6tX3+tUW3eZkBR9K7cYItCmRRcQ8gkHD7rwKtBiEVFUxSclR4siGJxfOMQss+X4PZCu2ahHDxdzPpnO+kdp8EmwQZqHxnKodxiXU44K08saG2b2jl0xqF3+srLkJ+D6vo+dp+EsKIIqmlarpM6AVfmHxZkJrqBHXOAiqvSQ06BE0e5m/ZNwzh4tgiPBp198MjItamknb5MqcuLx9dtHOKD46QEtUkR5udNbwomI08UJCX9NKGJTdfutwUoxDQJY/+pQjRNRFdOlAAAIABJREFUSFW8vDGTJmEWDueW0LXlTaIAuiFDL5q2qB/Mz5fKWPx4e5796ABer5fYJmGoKGSdc9AqIoS9pwTcZVIgoWkoJy+WsfX5boz7OB1HWSWZ50rY+vwdTN2UzeHcImIbhepaNjIgV7o9jFqTyv6ThdQJNIGmYTKb2DSyC5M+zdQzWxD2hXe9vY2QQBOK7z9Km6Z1mZecWE0y19jhOadfW55ZsY99pwUddfCK/cQ0suPVNLLPOdE0jbjGoSwY2K7a4iAplEZJ3yEf7gefxIbZrNZIsTSeW8oXV1Z5uPudH4iKsF21oBhhpmuNXxOwa4Nf5EJ4LXjn14wbOdb/tfbweywOv5lMMdAW2AusAFKBpUAdoMhvv0Lf62bgDsP2fwEdajjus8B+YH9IoxbaP/b8rCX8fat2/3vbtMzcQm3VrhNa6qlLWr+Fu7Qec3/UHpzzg/avzHNa3OQvta3pudrNPsng+L99pT045wctdvKX2p4TBdoDs3/Q5YP/lXlOazv1a+3heT9qe04UaA/7pIabT9istX55sxbz6pfa9px8rff8HdqeEwVa1CSxrde87VqbKVu11FOXtB5zf9T+lXlOaz5hs3bzhM3aQ3N/1OL/tkXrMXebtudEgdZmylfVXjNzC3U54ZhXhESxlFAW8/telw/OzC3Ufz59yaltTc/Vj1NUWqntOVGgNZ8gJJVvmbBZO5ZfrMW++qXWfMJmLTO3UJcjzi8u07bn5GunLzm1hL9v1bbn5GvJi1K0XvN3aL3n79B6L9ihPfDe99otEzZrfd/fKc77vpBW7rdol5aZW6j1W7hL6+vbVlRaqfWev0M7fcmp9Zj7YzUJ3vziMl3+V8r4Gq9D04TMbPLiFF1i2Xi98lpPX3JqfRfs1IpKK7XtOfm6nHG/Rbu0/OIyLfXUJa3nvO1a3wVCArjv+zu1XvN3aG3/vlW/L/F/+0o7ll+sy/hK+edbXxb3Z+CS3Vq/Rbu05MUp+r2SUr+9F+zQ4v+2RZc6ltd3+pJTu/Xlzfp9kNfiL+87cMluXfK436Jd2rH8Yi15cUo1OeT84jIteVGKPr9+i3ZpyUtStMzcQq3t1K+vkhyW8/OXGT6WX6zfV6Ncsb8EsFG2tyZJX+M5atrHeG4pe6xpmi7dLO9BbdLDNQ3/c/jP9XqfNf4s76txLr+lBPCNSi/fqMRxbZ/9LQc3KFNs5vrDDLQDRmmatkdRlNnAxGstMjWtJzUsMIuBxSBYNy99mulrijrMY4t246z0kBApaIfpZx1oCLExgBc/TkMDJn2WgdfrIdBkosSl8ebWI2TmleBye3ly+T6c5W7W+gyrQ6xmPF5Du7YXVK/G3G+Pc/xiKa9/mUW5BxSPphtWJy/Zg6Pczfvfn9CLqaculrJgQHsGfrCPGVtzaBkewux/HadVhI3+S/ZgswbQOiIEj8dDqRsGf7AHs1lky8EBviJzoIkx6w6RlVtMicvDxZJK+vuuOTgAZn59VNx4k0qo1cSc/m15fu0h7NYA4iPrUuF20yQsWC/COcrhyRX7aB9Zl9YNbIxcm0rrBjbMioLXq6GqCpnnSnU7wqb1QpifnMh9723n85FdsFstqKrQAOq9MIWtz3fjxKUyKt1ess45cVa49SapcevT9b+w1F9JXrwbFFVXfASROQ5ddZDFj7dj1jeCHvrWVnFd0gjjwJlCHBUuOtxyE7GNQ5m6KZvZ/doyZs0hsvMdaJpIRCZuyEBVFayqSlQjOzfXr0NS8zDMqsqUz7N0e8CH56fQOjyEpJvrUT8kUNRnesfjrHDTc36KwM/NJsatT8cSYCI+si7hIVadkifhi8TIMGZ8c5Ss3GK+HnunXj+AK1mdZNtM+jQTr0cTks4mRYdj5vQT2jrZ+U62+nD75YM7UlklnJI2jeyiN/EYIZIlgzrooloyu2wZIWieMmt3u8Vz2Kx+VxQWjc1IkqlTW3ZqfIIwctOllEBggLBllDLGki4pzyNrBsbifG1FW2uA6SrO+vUgkpoaoOQTgHxSuZahx6/NnGtrvPK/FuCGoCb/Y/8S7v5vPW6kGHsWOKtp2h7f7xsQgT/fB9nge71g2L+p4fORQN61ThBkMZHQRGi9e7xelvkaY1QF0nMF3h1sEo//CuLLb1bhlvrBPslihbAgM3/rLhqHbgqx0DrCRrvmYczcehRHpZcH5uzkcO4Vp6XESDshgSbmDkxk0cB2pOU6dX2WSreXgUv3smJwEvagACwWk6/Ya0LTNGb98yhJzcJQVYVDZ4txe7y80TueH8bfxTdju4nOXrdwjip1Cz71D+Pv4h/DhAdtdEM7b/eOJy4yjE+e68zUjVkoquorSprJOlfEvlOFvNI9irgmYTgr3Bw4U8woH/sj7YyDYauuFKUCzSYSI8OwWEzMTU4ktonoTj2S7+Tg2SKG3tkCEIqLEzeI5pFAs4lb69fRbfhURWHxEx3Y6tND2TrmDsJDrKx+uiM95u3izre/09viX76/NffP3qHzqGObhPHFqC56cJBsn5b169Bv8R72nrrM2HVpzOnXlhm94xm5JpVJn2WS0CSUSZ+K5rJZjyXo+ilHLjj5YlRXNo/uKuiqPWMwq6p4NamMWpWKxWxidv+2mE0qM/u2YcqmbD4f0RmLxcTsfm0Z93E6GXlFPDh7Bw/O2clHTycJk++1IkgtSG7HsieT9OAGVxpyjl8s5c1eccQ0Fs1XElKRzTjCkLxYWBO6PORccIIK8/on6lIF0oh765g7sAdZqtkCer0ar27MqlbMq6zyCJNzX8A1ioHJIRcQr6aRkVfMX9/brlviSUPvM5dLuGfmNt2QHK4ESrlNFp3Hrk+rxk2X8A1cgZGMcxz60QHBQDLkbbUVJuV2ecwbKWQaP3OtYmfgNYKl8fj/l2almn423h9/vfwbaar6Lbn7v3TcaDF2O/CMpmk5iqL8HQHdAFzSrhRj62ma9pKiKA8CI7lSjJ2jaVrHax2/ffsO2lffb2foh/tJPevQ7e8SI+2oqsKx/BK8Xg+gEN04lHcfS6DS7SXQrNJ9zk4+GS4C6CsbMsg6X8yW57vRfc4OYhqFMvmhaB6Ys/MqbXWJea5+piPd5+wERIv9h093ZNa3x3G5PZhVVTd0eGjuTjRN+NMeznWyZXRX6ocEMmbdIdweL6lnimgfWZeVQzrhKHfxpxnfoWmijhASaKJVuA2vonHAh7XP/e6EKL6eLaTUDV+O7kqTsGAcFS5e2nCYrLxiYhoJCVyrxUyFy83SJwVnfMy6Q6iKUIEEURxLO3OZb8fdrRtOgODQj1l3iIUD2zNo+R4CTSYsZsEiefbD/ew/VcjXY7vpXPrKKg93vf0d37xwF+PWp5ORV4zm9bLm2dt5/UtRjHW7vRzJd7JpZBedOy6pgxJXHrbqAHt/vszaIZ2Y9a3I5t/uHU+4LUgv5E1/JJbuc3aIRqwB7XR82B5kYciH+1nyhMCHh685iIqCy+Xh2KVSPhjUXle1lBmysYAoBcHkz6PWprLv50K2vXQXkz7NrFZUlOPp5fvIOu/QjbhHrk3V6an+Was8z5AP9zP9EZHRz+zbRnTjVl2xxJPnqC07lHx4mdUD1bjr/pIGxs8+vWKfeFrxactLumul+4rAWU3yA8Ys2L/gWtvP/pm4cRiPXRPnvjZO+y/N6I3nrimTrukYxn1/LV++pp9ru5ZrUU5v5Lp/7fitJRBGAasVRUlHYPbTgTeBPyuKcgz4s+93gC3AT8BxYAkw/HoHl3rc4/7SGgU4c7EME6L1XkWh+U1BaBqUu0W35Yvr0un9fgoTP8mgRf1gHpm3g17zd7L3tGBAOCvceL2CtWHxdbdWeqGORWSMiU3thFjNrH6mI9M2ZWMPCuDT4Z358OmODF6+j9d6xrBwYHtQYOKGDCZ9lslt4SEseaI9py+WYVZg2hfZjN9wmGk9Y7EGmFnzTCeOXSr1ydJWiQYsRaFNEztLnujA/jNFVFQJEv1bW47g9nrxeq/IM0zZKDLrVzdmsXBge9Y+eztosP90ES8/0BqzquqNMhPua43q00W3BpgY9adbKXV5cVRU6ZmjNUA4F5lVFUeFi5MXy8CLziLJyXfSNjKUaZuP6I/olW4PjoorAmCbRnYhvmldWoTbWPZkEkue6MDCx9sT4+sslRmmqiqMWSP0VEKDLczu15a2TcPot3gPb/eOZ1rPWHrOT6HAWS6YOz55ZYA3HxXCXsaOUpkZj12fxoLkdsxLTsRiMbF+aCfe2pKDPSiAt3vH6/vIL/bTK/bx11k/isIjgtWy+PEOdG55E4FmHxzh9lTLYiurPKgmRdehH73uENMfEYu7fMKQ91O+WgOEVtCrG7NAucKekd2fMujV1OAjfx+26gBd3vxOl1MA9EYlmf1J4bKrJAIUBdWk8OrGLF1AbMzaQ/SYt4upPaL16zBmmIG+v5WxiGm8Njk/KVFQW0Az7iu315apGu9ZTduvNWr6zI3SJn/Jvtc6b00/13Ytkp57rfP9J1k6f4iGqXrNo7WGA9/R2SDrn+3EG1uyOXbeQYkvECrAu33i+fumLNweD60a2pnSI1bvTg0JULitoZ1j5x20bmDnwNli7IEmvhxzB84KNy9/ks7Rc07KvcI4/Mh5wU6xWRQ+euZ2Hl+6G6dLw2ZRiGtSl7f7xFPp9jJ1UzYvP9Bad3FKbGpnykOx3FI/BEeFi+5zdnDzTcH8fLGUL8d0w261MOzDA6ScvExUo2COnCvD5mOcJDUPo8RVxdz+7Zj8aSYuj4ej+U4+eqYTTy7fx4rBSdzkk3m4551trHlGsGykSXmnm4VAl2xAWvpkEgUlFdz77naiG9UhwhbMhPtaEdM4TIdVnl2xXzBlqjzk5Dv4dtzdQvbW5casqnomPqN3PI6KKh6Zt4MdE+/VKYXTe8XStJ7oQTAGVFVRdJ304xcc/Pnd7XS8uS6v94pj6qZsKird5FxwsnRQB4Z+dIDlg5OY890JJj3Qmmmbj+Byecg8V0Rs4zCmPRLLtM1HdFx77Lo0vF6Nab1isVuFhpGRUjg/uR1LdpzE7fbqjBOJ207uHkVk3Tr677Ie8OrGLF7rGcPD81OIbWRnSo9o7NYAvdu3wFlOuC2Iwcv3kp1XTHSjUBY+3r7a0wJcse+DK1Z/8hjGDFc+oYxbn67rtsvts/omMHJNKlN6ROtPU5L5IkW45BPCyDWp+n32n4c89wVHuTAPd3kwB6h6bSHQbKqxGcxI4/RntPiLgAE1du36Uw2Nw39Ru1aGbBw3yqKR+8muYv/3/gi0x3/X+K/qjJX0yuAAoVrpr9YoterLPdVVJ/3pjwlNbBzOdRLks5mzWVRMvsadfaeLaj2/pPWBwOi9vkKqCgI3V1X2nCoErlAoO7eoh9etsed0oX6cjT5+t8vr4cDpK/oogYDFotIiog5pZ50+yz8Fp482umV0V/ot2q1fc6fmdUGFiko3aXlO4hrbycxz0K5pKEfOFesWeiFWC6qicLG0nGyf2ubTHx7gy9FdGbBkDy3DQzhwuojEpqGoKOw7U8T2l4Sy47iP03W5gOFrDpJxtoiSCg8e0DtcPR4PJpOJb3xBQy9arjqIF40j5xx8PfZOvTM2JECh3K3Rrnldjp534PXZDNosQod+7bO36y5Jkz/LJOtcMV6fMYw0GPnzu9vQvBoOl1d38goJNNGqgY3sc0WgCFvHVU8lMWrdIV1HBmBy9yj6LtzN+mG3M3VTNq4qoTl/OLeQ+CZ1dSXKiyWVPDR3JzarmS98tn1d3vyO718U8I7L7eHohZKr4CkQzlPfj7sLEEqYmeccrB/aielbclgwoJ2+wMzoHa/DX1ERNr0rV9ItR65NxevRRAeyj48OVDMRGbk2lcNni4iPDLvKzm/sujS90CvlEKTkx9h/pDHrsYSrgrNcpCSMA1SDcuQCYIRhagv+/gvb6HWH8PqUOY2dxfIcwFW8fSMEYzRVuZFAfcFRXmMX8o1AO/7z/73Gv+Mc/2XqleIR2aSK6TQKE3+44ACFYDMEqAour1ButFrNtGsWSkITG2az+FyQKnjgJy+VExygEBdZl6TmYWx5vhvxTcOY2jO22vliG4foKpBxTWyc8Ik/KcBHT3fE7IMVPnwqieMFJbgMbJ1gs+CUT+sZi2pSMClCH8cEDPpgnyg83ttK379lRBBViAKy1WzGqghIoHl4sL7PlI2ZNL8pSJ+b2aTyTp82nCosJ6ZxHWxWM7ZAE2aTismkChvDALNge/SI5uj5UlRg2Y6T/POFbrQItxHVUNQ3bFYTxy44cfncVl5Ym8b4DYeZ1z+RmX3biMJfcjs2j76D21vexKqnkhi2OpVb69dBVYWq4dh1aYxed0gvWprNql5MrnR72O8L8mazma/HdmN+ciJtmgqRtLAgM1EN7SiKUMrcOKIz03zNVy3DQ2jV0I49KIDZvmxeURThY6uK4nu7m+sS00QsVGVVokDfrlkYI1YfIKqhDXuQRS8uSl37Pgt3i2BdUMIr3aModXl5s3ccIOzyBizdS7umYWx4rjM954snwqSb6xFoNrFkUAdWPt2JTSO70HN+CpVVHub0a4vZrLJgYDvd2Wjs+jSm94rl8xGd6fN+SjXIxOvVhICZD/7KKSjRBdDkPjP7tMFsVpnXP7FaYHx1Y5beuLTkiQ58M/ZOvdMZ0BcQVVX0Qq9XE8XdOf3aYg8SDKrxGw5fxfKQBeWx69OqGY8YC4/+WHtggEkXIzMO+TkJ3czp17aaiJmEMuS1SSjF/+fhqw/qWvdGY5PrjQh7ULUgX9Nxr1UY/b07W/8d5/gl4w+R0d90c7S2fVcKJRVu+i7eozc5rXpKdDW2DA+hwu0m81wpSc3CeKV7NE+tPMAbPWMZulqwKNo3DWXsn1vx3Kr9fDbyDt14WUIQRWUVZOSVVBNAa1TPyrQe8byxJZu0sw7qWBS9czTQrGK3WrjjzW+Ji6xLeZWb4+edmEwmVj4lYIipPaJ5cX067/gs5gAdex68Yi+nCkoAhebhdcg5X8LigUIO2KqI7tuYxiFM6R7LjK05+hOHCejYoh6Tu0cz+bMMDp4p5p8vdGPK51ksGNCOgpIKJnycznv92/LQ3J18M/ZORvlYLu/7lAMlVc+rabz5aBwTP8mgwuUm53wxG0d1IzzEKlyZ5u5k06iuujriaz1j6DF3J6uHdNK7TWf3a6vDD3BF9VB2iALcO/MHANYN7UyLcJuu0z59Sw5Te0Rjt1ooKKlg6qZsvQknt6iMh+bupI5F5csx3Qg0m7hv9g5uvSkYa6CZKQ9F67BNoFnUD/787jY+Gd6Fkgo3g1fsZ5NPSldmThcc5dXEvhzlLgLNJoatOlBNSkHq9BjFzIatOkDOeSff+wqy0pJP3k9HuesqOEPSUx+au5P4JmHVCr3+kIg8t4Rj/LVujMO4TTZBSWql1OiZ0Tv+Ko11+dmaYBm5X20F15pegasgGiP0Ulv2XNsx/I9n3Ffe699qXC+b/v8Z/X9gNKsXzBtbcnT6l+xkfffbo3i9Xo5dKNElDNyal97vpzDnsQSGGwSssvKKGfjBPpwujZc3HCYjr4hhHx4Q2KXbw+G8EuoECGgGhJJkzvky+i7eQ+pZB0FmUFUTD763nb/O2g4Iu7tSl8bk7tH8XFCKyaRy801BPLXyAJMeaM2kzzI5ct5B9zk7eHFdOj3m7aLS7eEvs7aTmVdCuVu4SR05V8KGYZ1pGCbMwCs0kZlm5pXw1PK95OQLameo1UyHm+sy4T7hpHTswpWFSVVEIfHRBSkcOFOMs8KtX7uqKhw8U8yYdYf0rGheciKqohBuC2L8X1uRluugpEqoSQ778ADd5+zA4/Uy6bNMZvSOF9mg1YIGvLElh5l92+gFYFmUlNCNPcii489j16eh+ozGBy7bR2WVh6k9on12ftG8ujGL4asP0uf9FNxerx7oBizdS9vIUOKb1sVutTBufTqfj+hMsDWAmX3aMG3zEcb4DL1HrknVs/2XPzlMzwUpLH+yA1M2ZetUvOKyKyJrUzZlU1klFvjhqw6Cl2puR0ZeOIj3zKrKVp/7lHRSmvhphnBP8hV5jYVTe5BFlzuOjwxjZt82jF53SC9sG4cx65QQh+Siy0VZFoZl0JOyvNKEGwTlUZpzS411uBJk5X2Q+/sP47z8aYzGzF5uB67C5Y3SxEb5XzmudYyaCqTy2Mbruda40Qz5Rgq2v/f4I9UK/hCBHgTL45MRXVC5YsH3t+4xmM1mbosIETouzcJ82udhzP72GHGNbULtMkAhLjKM9k1D6XSL8O+MixQ893n9Exn3FwGltGhgZ7bPxENVFGwWlQ+eaE+o1URAgJm1z3bSNdhf3ZhFpdtDaHAA9UMCiWpop1WEqAEsHJDItM1HmNmnDXFNwoSuu1kwN8JtQawZ0pHOLeqxcnASKrB2SCdmfnOM+iGBBJsgqVkYX4/tJgTLzCZuiwih48112Ty6K3OTE5nz3Qm2jO7KuqG3A/DKZxlCYMtsIqqhjcRIO03CgomPDMMeZOHt3vHYfc4iMqDJppefCpz0WbSb9/q2waRAq4gQ3u4bj6oKobh5/RN1HfNKt4fYxqE6XdDr1egxbxev9Yypxjcf+tEBoQrq8bIguR1fjOrKqcIK1g8VkkYvrT9M6wY27FbBQZ/1WAJfj71T6L37FqGvn+9GcGCAfsyMvGICzeJxX3LXzSaV2CahTO8Vy5RN2XwxqisfPXM7W0Z3JapRqO7pKgXBJGRi/F1qQMzwsXSMTJQhK/czck2qgB0GdSDCHkRggEkvhurNQ4pCVCO77qUKVzxnAwNMOovI6xUB3MgZN76CwKsXDGh3xTu3ynMFd1+fpmPP0mbQX9NdLrQ1FSzlfK83ZMA12g4atxubrYznMPrSSvz9Ro5dG5NF3pvaGDI1cfOvx1X//6Pm8YcI9EfznRQ4y7FbA6hjEUE7MdJO47Agfnjxbt7q0wab1cS0nrFM3JBBdl4R+04X8VNBCSGBKi3C62A2qVhMJl6+P0ovZkk63ax/HsOqwE/5Tp7+8AAq8MHgjphMKku2/0xUAztrhnTi9c1HSD1bzOu94vTMcNNIoa9+9EIJr3SPJiRQ5ZmV+3C5PfoX7u3e8agozHosgQJnOQ/N3cmIu29lxOoDhASamPXtMdLPFpJXVE6ZR3RVTv4sk6GrDrJicBI/XSrD7fEycUMG49an82avON7xLQztm4Yyt38iqiq6P7PPOUg766DS7RGm1cCrG7NoFWFj4cD2zOgdr3+5ZvSO5/Fl+1g+qANj1qUR0zgES4AJu9XCx8NuZ9rmIzrtDtAlncetT8dR7mLBwHZENbDpNL7R6w7phcbpvWLJuSAa0OxWC5/7sPdnP9zP3lOXcXu8DF99kGEfHuC+2TtwVFTpjk1j16cJiMtnuA0Q09jO2HWigWfIyv36grAguZ3ODZ+yKZsCZzkDl+5lyIf7ddhFBggZPK0+Fo7ZrPLmo3Fkn3cwZu0hXW4Y0NUpvZpQqCxwluuBZOy6NAYv26tn6EsGdWC2Qa2xwsDoqazy8Nf3tuvsGOM+w1cf5MzlkmoZriyISthI4tvy/5LEnmUvgH+2LGmTxm1Gj1ag2tNKbcNYoDUOSec0DvnUIXF/QFf+rO3Yxtcbwcr9IaxfinH/0TBx//GfntcfItC3CK/Dw/NTGPHRQZwuDZdH+I3++d1t5BaV0XvBLjxeL/0WpVBWWaVzz6MahtIoLFBYELrcZOQV0nNBCpMeaK0fe+w/0sjMK6ZCEzBKsFmIWM365hi3hYeAAjkXnAxYsodZ/RL4/sU7mbopm7Hr0pjaI5pJn2ZS6fbQsn4dBq/Yj8fjFcVj7cqX4sHZ28nIK2bsujQmfpJBHYuJJz7Yh8fj5baIEN7uHY+iCCen9c924r3ktiJLbGhj1rfHfZ25Diqq3HrmPqtvAmP/kUbqmWIq3R5cLg8TP8lA9XXQShZMZZWHyd2jSD1bREFJBX+d9aP+eB1hD+IfQzuxaPvPWM2Qfa4Ed5UIwI8u2IXLdYVjvWRQB2b2bUPOBSeuKg/3zd5BpVtkrRIjlkXP8RsO8+rGLLb67PXufucHJn0qICCrRRRkVRS8mhDQ+mBQe6ZuyiamsV0PaKHBFqY/EqsH56k9YsRCUOEiI69YXxAq3R49AM7qm8CkTzP5fGQX5vVPrAbHGHnMxoJjuC2I28JDROEzOZElTwg4c/S6Q7oC408FTu58exsFznLm9GsrmsIuCKs+6cA0bn06Xq+mG5fI+wYQ49OXNzarWQOExPDDPm57YIBJlzaQc9a9VQNMenA14tqy2Gl0VJJ/A6BagDcWV4FqmW9NQUYG77sNTlFyIampVV8KtAUGmPR7d70hF6HaArAxk/cP1LXBPLVx1W/0qeD3Gtc6z/U6ff8d4w8R6M8VV/D5iM6oPhbNyYtlglIXYWPA0r20bGCjvErD6/Xy8+VyEiLtmBQYc+9t5OSXs2hAIoue6EBUQ2Hp5nJ7uX/2Dmb2bcOCAe2IbRJKnQBxseVuOHHBSfZ5B8cKSpjdry3bxt+j847DbUF4vRqZ54p5af1hDucWMWp1KscvlrJoYDufybhNb1gCge3HNLKzYKBoq/9yzB3UsaiUuSH1jKBZxjYOxVHhInnpXh6cLWoAs/u1ZXL3KJ5bncqGYbeLzN7rFZmk28OsxxKwWYUkw/4zhbz5aBxfjOrCz5eveJaOXneIqZuyad+sriheGuZVXOai78LdvHx/a8rdsGlkV1YO6eSTGxCUR6OHpz1I4M2LB3UQ7BIfHAHo+8hsVFrGBQaYhNFJcqKOfdutAew/UwheoQ8vWTAygMhg3HN+Cq/1jGHk2lT6LNxNq/AQ7FaLaMgKsjC1RzT3z95RLbgezi3ipfWHa/x/JAOGrCWEBltwlLs4eKaI6Y/EEhpsqcYIWfZkEvOSE3nnm2P884Vuer/A0YtXqJXSos/r1XB7vdzAdpKLAAAgAElEQVT33nYqqzx61iwpjzLoG5usAs3CKnHSZ5kM/ejAVZDIrL4JepOYLFbKwH0tXZTR6w7x9Ip9eoCXjWo17V9bpivrOHFNQq+Ce/x/l/dLnkPaBF6v2Hkj1+IPG10L5qltW23v/buy/Gudx/j/8T+K2d+I8tnv/S+hbaKmaUIVr9e87bq6oVRLlMqEUgUxv7hM67dol1ZUWqn9ZdZ3WvKiFG3gkt1aZm6h1mbKVl3hT46i0kqt74KdWq9527W+C3dqfRfs1I+raUKdr+/8nVri/35TTbEwv7hM67dwlzZw6W5dDbHNlK+03vN3aAOX7tZV7KRiYbXzvb9T6z1/hxb76pfa6UtO7Ylle7Ryl1vbc6JA67dwl1A9fH+X1nbq19WudeCS3drpS06t3f9+o6tRnr7k1OL/tkVXDZRqhpqm6WqEUi3R/7oHLhVKkwl/36orIsr3pFqh/2fkdSUvTtES/r5V32Z8zzj8t5W73FrykiuKj6cvObWBS3fr98B4Lrm/vPai0kr9dfDyvdXunbyvbaZ8pd/zmtQAjduLSiurXUNNw3/uA5deOb/xbyzvcbV9fXOV4/Qlp/6evLfXUju83r2saR//v3e7//2m2rxquqbaxq9RlbzR8WtUHq93vF/6/m+tFvlLzv3vmAM3qF75h8joj+aXcMFRjqPCxaGzxbz4cTo95u7EUe5ixEcHGb7qIM4KN/tPF5FXVM7YdWmoKOw+cZGc82UUVVTgcnlE9h9ep8aC1JF8B8cuOHn5/iiOXSzl5MVSKt0eLjjK+fPMbew7XcgHg9rrDAlVEY5AXo/G9EdiGbh0L9N7xRLTOJTjF0uZ2aeNvkKPW59Ope8xVar7ZeYW8kr3aEqqNF78RzozesdT4Cyn7+I9ZJwt5KX1h8k8VyS44FaLyPh8bfrhtiC+GnMH4bYg4iPDACir0igoqeDp5fv4y7vbdQaILNJNuK8V98/eoV+vzCTm9U8k0GwitnGoXkyUxVqZYRobTiQGXOAsBw2iG9n1ztN7Zm7j6RX7riqI+Rc5QUAx8ph265WionEYpQIku0e+BgaYmNojmnCboNw5yl0MX3XQlzEreh2htkxKbpcFZuM246jps/P6J+qFT4n3T/w0Q8fSjZ8zYtVnLpfwp5k/cuZySbWipH9B0jj8t9fEYvHHuqVT1sRPM/TisZG14k/r+yVZ8G+ZdV7run/puF52/p/G6H/JPf5PjD9EoNfQGPHRQSZ+kkGbJnamPBRDTONQTl4sZd+ZItLPFjLls0yWPdGeJ5fv4/DZQsqqqhi2OpWbwy2cuVSOy+Nh0cB2HDpbzP6fL3HPOz+QlVek/+Fb1K+Do9LLwCUpTH6gNX0X76Hj9O947qMDKKpKQlM7gz7YS1ZeEc8s34dX0/j5Ygl7ThdyqcRFtE8LZXL3aD4edjuBZkGDK3CWczi3iKc+2Mudb3/H8NUHGfeX23yqmkIywesV1wfCkCSqUShj/3Ibjkovk7uLRhuXS3DLZYFv/IbDOMpdTHlIBDtpgvFK9yi8oC9SFVUejl9w8NDcnSwb1L4aj1syKEavO8TUh2OEt64vKA9etpchK/fjKHdVY5LM6deW13rG8NDcnXg1Te8IDQ228P24u1j2ZFI1nLS4zMWwDw9QUeHWlRsHLdnDve9up9tb/2LQsj3cM3ObDr/IL6PUdKmo8pCVV8SfZv7IhPta6SqXjnIX97yzjZ8KnHonaOa5YiZ3jyauSajullSTBG5llYc3e8VR6as1uKqqy/bKBbmiysPTy/cxZKVQfTRi3hLnN8oWBAaYGLJyfzUVy6zzDp5dsZ8LjnJe3ZjF5yO66LRP/wB9Pcy8pm1DVu7X75MxkBmvJ8IeVO3a/GmQ/uNa7xnP/WuC5i8pnv6SURO0498/UJNJyS8N/v/pounvNf4QDVOBjW7Tmgx6j7hIG+lnnUJ6oFkYPxWU0CjMyplLpTh97kwJkXZOXizF7fZQ7hYOSVKWwB6o4tWgxOUlrrGNjDwnSc2EbrnRHNw47D7FymlfZHHgTLF+rMRIOz9fKqNxXSvniippcVMwB84UV/tcSaUHm9VMi/rBpOc6CDIrJDSrx/i/tqLnghTim9jIynPi8d3i+MYhgs/vk2gA0bgV3TCUvaeF41FSszCsFjMut4cj5x0UV3hIai7ckY7kO2gRXofUMw7aNw0lLddB+8gwcgqcuu68f+OOLBhn5Bahqio/vHi33nwU1dBOTr6T+CZhAkt/P4UYX/abmVfMisFJPLIghR9fukvHr6F6Q8zgZXvZc/IyqgpJzerxdt94Jm7IwOXxMPXhWN7aepTJ3aOYuilbF1QLNJu4Z+Y2WoeLY+47fZkNwzozY+tR9py8zPcv3km4LYgBi1NIy3WQ1ExIQng9GjkXnMQ2DuXNR+N4dWPWVWqMQ1buF05TmsYP4+8ht6iMh+fvYueEe4iwB3HmcgkPzd2Joihs8jU73RYeQurZIjq1uIkFyQIrl1IBxrb84jIX98zcpnekWgNMZOUV0X3OTjrdUg8U4b0ree7+Nn1ywTBqzcv7WVMjkXyKkgbg/nx3WYcw/l2Grz7I1B7RTNmUXWNjkpQ08JcO8F+U/LVxbmTUdB2/dr8baXi6noF5bffoWuf8vU3Ff+vxX6V1E9y4lXb/qx/w08Uybq4XhNmkYlZUJveI5qG5O0lsFkbOuWJU1QRotIqwkZPv5Jb6waTlOln/bCdCrGZsVjMvrEvjle7RvPVVDpl5xcRHhvHmo3E8OHs7jsorwjgKENckBKvJTE6+g7IqLx4vtI20YzabeLdvG15Yl8a+00VCZO2rbFLPOJjXL4FXP8+idUQIUx6OYeomYXxRUeXm5OVyNo0UGd1TXZozcq2QEpj2SBxTNmXh9gmSmRTBrX/jqyOYFYU3esfz11nbWTk4iXifrIDs8Hzx43TwiAJhi5uCOXGxFEe5m20+zRp7kCg4jll7iNn9BRtj/IbDeDVNaKmoCtN7xTLxkwzefDSOpvVCKC5zcfc7P7D6mY688VUO0x+JFaYdESH6MaReysWSSmIah+n3TRbZjG37k7tHATD500yyzhfTKsKG16vxc2E564d2YvKnmRzJd9IqIoSjBSX88OLdutDYjN7xjFl3SJccNm4fuy6NN3vHYbdaGL3uEK/1jGHSZ5m8fH9rXTOnZYS92hcU0CmDks0y+k+3EtUoVA+c8jqlJPK8/lcWIPkZ2f0rM3n5xZeNVHqG/+F+0k5f5ssx3fR5S0MWf0E0OYzCaMb7WlPglx25/gGqNhljo6CasXPWGMCMejbGgOjvIyuPa2TC3EgA9l9crref/3aoXWLY+Lna5JFrO8dvscD80cZ/VWdskMXE+wPbs/qZjoQEWTArKtn5DuqHBNKheV3efDSeNk3rsXl0V2IahfJuvwRAY/xfo1ARjkzTvsjmxXXp7DtdxLRNoqU/upGdBQPaEW4LYvuE/2HVU0n6OetYVE5fKkdFIbZJGJtGdqVd01CsAWbmJwsJ2nf7JRASoDDrn8f4qUCIht0b24gvRnXBYjExbVM2OeeduD1efr5UVo2pMf+7E9xyUzDHC0p4Y0sO7/Ztg8VswgS0bRrGzK+PcjzfwYHTRQSaVTreUo9FP/7M8FUHdcnfpvVCWPx4B6w+SeWQIAtrhnQisWko4bYgIuxB+n/K7PNClE12h87rn6hDLxM3ZODVNCZ9mql/OTaN7MLjy/Yxs49wnPp+3F0sfLw94zccFkqRbi9/fW87yYt36xCRHBKXtgYIDv7UTdlM+VxI9iqKQk6+k0O5xax6Oompm7LJPFfEx8/djiVANHxJ3XSJeZtVlQJnOaPXHcJuteD2eBmz5hBZ5x3YrRYdk56yKZspD0UzcOle3h+QyGOL9ugwhJHNEuirW1gDTEx6oDV9Fu1m8LK9BAaY9OuUWLvE4+1BFt1UQvqmTu0Rzeh1h3T4BNBpnnLfmX3aoKpCLkNm8kbYR8JjQ1bu12Ez2VTlDz1A9QYiQD/e0I8OXCVpXJOuS2iwYCv5d87Kff2Do2T/yOuRPHl5L+XCboSsrgVv+C9Wte1bW5CXTxI1KWMajylrUDcyF+Pv18vW/1P0zN97/CECvabBiFUHeXDOTqY8FM3s5LbcFh4immzyiuj9fgruKpGNZ50r5lKJC0ellyc+2EdwgEKF203WuWJe7x1HHTMcveDE5dNcLyipYMjK/RSUVDD/uxO0bhiEzaKioNGqgQ1zgIqqKEz9PIuDZ4rJyrvCW690eymr0hhxz62AsDJ8fMluXlp/WGiWK3BbhGhCah1hI9As9EgcFS5STl7mcK6DqioPLz/QmokbMjCbVNo1C2Ny92iOFpTQqoGd4ABR9J3WM5Z5yYm43B52n7zMKF8wcFS4mNw9ioFL9zLhvlb0X7yHA2eKdbeniioPY9YcQlEUHBVVuKpE67+jwkXWuWLcXi8utwevR6PC5dbx53BbEBtHdNYDnoR6JtzXCq9Xw2IxsfqZjtwWYWPsP9KqFVsl1q9/ThE1hoWPt+eLUV35cswdtI0MpUW4jem9Yimr0gj08djNJpXxGw7rnZ8g5Ch6zN2J2+szgVcVzGZVNKv55iU161/ZkMGt9eswbHUqHz2dpGvAyPnIYFFc5uKCo5zpW3L4fEQXnfYoaxZDVu5nyIf79fthDIay61f2Bhgpk/IYsjYgC91AteuSx8rIE85gh3OFlpHU76+pkGwMZDLQyjqL2+3lPoOrV01BHEQg7zF3py6yJoejXEg53OPHm5f3Vh7DqGMv5yHnHOi3YFxrWAN+nXWeMYm4Frf+epj9teZ1o8P//P/OoP9bn+sPA920ePo9yt0abSNDQUPXk3dWekhsFspPBaXcFhHCwdNFBAcoQl8lIoSDPjlge6CJpYM60HfxHsyqkDZu3TCUExdLqapyU+FBx8rrmCEgwEzrcBuLn+xApdvDqNWp7D9dSNvIUMwmlQOnC0mMDONIvoPyKi+xje2knXVgs6iUury0bxbGsYJSQGPF4CQGr9hPTCM7qqIwpUc09767nVl94hn78WHsgSYUBdY+e7to+qrS2DSyC1M3ZrHndCEJjW2k5zn5cnRX+i/eDSgoCqwZ0okH5uzEHqgS3SgUS4CJiko3+04XsXfSn3SI4J6Z2/hgUHsGr9iPpmlEN7CjmhQyzhahoVFW5SXIrFDuhq3P36HrwR+54CSuseDAj1yTSvrZIoor3IQGmvhkRBd6v5+Co9xNp5vrsfCJ9jpu3e2tf5HQrJ7OpZZ69J8N78xTKw9wS70gDpwp5tsXhHuVUXo3MMCEo9ylwx+A3m0rcW+JT0uOuuwiHbbqACk/XaaTT/e+78LdbH2+m67mKOGkISv340UjK7eYTaO6MnFDBlnnHXz9fLdquu5ycTDy7uVCIcXD5LmNUIfMOuf0a8ton8PYggHtqs3XCPWMXZcGKiwblKRDJ/7wihzXgjSMblr+EsASTnOUu/jzu9uIaxyG2azq87/nnR+IbmznzV5xuhBcTTCGMcDUJGgmC8SZ5xx67aCm8WvwbiMseCPwz//1fDc6J+Oic6PH/79AQL/kXDcK3fzHOfSaplG3WZTWZspX2p4TBVqLiZu1Nn/7Soub/KWWeuqSFv+3r7TkxSk6nzozt1DnoaeeuqQ1n7BZe+C977Xe83doCX/fqvWct13LzC3UEv6+Veu3aJfWe/4O7ZYJm7Uec3/U4v+2Rft0/ykteXGKlnrqkhY3+Uudv91v0S6t7/s7teRFKVry4hTtWH6xlrxY/JyZW6gVlVZqvefv0Pq+v1M7ll+s85iNnHfJmc4vLtPiJn+p9Vu4S+sx90et30Ixj+RFKVr837ZombmFmqYJ/n78377S+i3apWXmFuoc7mP5xfqxes/fUe3Yx/KLteYTNlfjaxs53pJfLjn0py859e3V3vPx9SXH9/Qlp3Ysv1i7ZcJm7Vh+cbW5GPnQpy85tVtf/lI/v6YJXndmbqE2ePlecd8WiXv2xLI92hPL9lzF2R+8fK9+3+X7/rzvcpe72nvys8fyi/XrMPYy+H9eXn9RaaWWvEj8/5G8eLmPPKaRMy/fMx7H/z3jP8lpN/5e23UYh/F+3CjH3v+4xjnJcyQvTqnWZ2A8X35xWY3XWdt5r/X+teZtnOMvHf8XzvnvzZm/0ePL/1v/jmvhv4lHH1k3CFVVaRRmpeMt9fhkRBc0TeMNn0+pLCK+1jOGN77KITOviOfXHuLxpbsBOH2pDLNZpXUDGycuOKkfEsg3Y+9k+eCOvNsvAZvVjIpY1MZ+fJiSChfJi1JwujQullTi9WpCA75vG97sHSdUH0OsAtJ5OIaBS/dSUFKB2aySfa6Y8BCr3hnatF6IznmXkgQjPjpIuRvKXFWculTGhPtbc/xiKePva0VJpZdpm7KpqBLFv+iG4ingra1HqazyMOWhaB5bJHzYHeUujhWU8NL6w0KFEbBbA7AHCr0aufIHmsWjfaDZpJtwhAZbmNmnjS7ONn7DYQLNV1rtvV6B2TvKXT498B8BsFlN2K0BonOyfyLTt+RQ4CzXKXtTNmXrrBhAL+y+vvkIU3tEM3VTNlnnRX1FUjVHrztUjbO/YEA7XTRNttXL4/tnq4GGx3SAyZ9l8pd3twkNGr/uTOMjvhQEA3Ss37/IKDuLjVCM/zB2NRoVGeFKt7CxP0DixvJvAyLz9898JU/fvwZgvBZ/k2v/4xq7SuW9UhWhFRVors5hDwww6U9Rxs9d67z+TBzjvZN/s9poof56ObWNmo79a8fvXUS90ePfSC3gtzrXjY4/RKDPKxZslUmfZmI2qbjcXkqqRDC6LTyEiRsyOH7BQc/5Kbx8f2tAIfVMMe8PFF/kqEahzO7Xltd7xeGoFIW88RsOU1nl4dWNWbQMr0PaWQfNw4Vt20+XhLhY26ahNAkLFsyUR2LpMXcn3efsEHK6PqOIqZuyua1BCI/O34nL7aGs6sp7IP6jyjb2WX0TqHR7OJRXzOKB7Tic6+SWm4J5cvk+WobXYcbWo4QEmvCi6dBEzgUnsx5L4LWeMQxfdZDe76ewcURnAgNE0L4tPARUyDwncPlxH6dXw4wXDBCa+5nniq+a17j16biqPPSYt4uKCjeOCpeO+aqqgsvt4S/vbiPQbOK7cXcSHmJFURTGrhPBSuLiEvOVQTrcFqR/yQMDTMQ0toMixNWkMuX4DWJx6jFvFy5DAJavkoMOYkGTBUEpNiaHMZiMXJtKZp5wmZLXKqEEKfVrDByVvvnJNn9//1WpcCldoYyBVAY6I6Qj4RaJERvnXJskL1CtaOgf2Gb0jtev14idGz8vaw5SFK2mICLPuWRQh6s0deT7RqqhXFT9g7U8r7HmUVtAry2Y3Wigu17B9kbG9T57o8f+tXP4bynW/iECffN6dXRdlemPxPL65iOowNRHYjlyvpjMvGIeXbCL2Y8l8MaWHFo3sBESqNK8fjCJkXbeeFRQ8Sb4lBAnPNBa1+aY0Tses6ISZIZTF0pJbGrnH892IqlZGO/0aXNF3tZq4ePnOhMXGYbXrTFmzSHmJScyLzmR1x+JQ1GFOqbULJcsBGuAEK8qcJbrzIXPR3RhecopQgJVzKp40rCYTMzql8Ct4XU4ekE4Ds1LTtTplA/NFQsJCDVIEEYgOfkOpvWMJbqhnUmfZjKzTxthf+fLqKTuuuLr5JXqlXIsfLw9y5/swJF8B3fPEMJd1oArevUoCgUlFby0/jCVbg/RjYQzlez0BUBRmLgho1qWZ8wMlw1KYsGAdnpWaQ+y8FrPGJYNTmL1Mx0xm1Q9cEgGz/gNh4lqYKOgpILbp39XbRGSQy4AgP6EER9Zlw3P3c649ek8vXyfvjDMS07Uza/lef7ka9Sa2UeoShqzc4l5y25cYxHQmMHLgCm3j99w+Koipnwi8NeiNy4IUF2r/czlEu555wfGrBPsogKnmK9Uu/THeI2iaDUNY8AxZu5yu/FaJJe+tkXDUe6q5vhUU1Pa9TL2a9EXjfv80sy3tuJ1bfveyELyaxec2j73Wyxgv/X4QxRjE9u117SHpwMK0Q1sTOsVyyPzdvDZyDv487vb+eipJJ5bdUAYbDcTOvM5+U5uqRdMaq5D2NiZhO3dqP9pyQe7TumFv6eX7yP9bKHuz2qzCDs+t9tDSZXGty90o8/C3bSsX4cTl8pYP7QTjy5IwVkhuOqTPs0k/WwhrSJsmE0qXk3jyHkHiqIS28jO1J4x9Hl/N8XlVXz4VBIjVh9ANZlY/mQH3thyhOxzxcQ2CsOrabz+aBz3vrudVU8lsWTHSb1QNnzVQdLPFhLV0I7FYmJBsijsuVwe9pwupFOzusJb1KTgdnk5dqlUt7STX+azhaWEh1i5550f+P7Fu3U2yms9Y/jTOz/SNjIUr6ZhDTCzbLCgmQ7+YC9eTSM7rwiHS6NTs7qYA1RmPZbAuPXpHM4tIrqBnbf7xuvNSYDuobpgYDs9y5W0TslY2X+6kI+H3k7vhSkkRoYRbA0Qc5n5I9+NuxO71aJ7z+79+TK333KTPi8573ve2UbSzfWw+HTqQSywKAiFyXwnm0Z24eH5KWz1yQAYs+DXesYw6dNMMvKK+cF3T2QAl5K+0mvV69V09yspFmYs0sKVp4clg65ARhVVorsWhJ/s5yM6X9VcZoSi5N9b7iudsKTJtz933whHQXW3Krndv3jnX0CUC4Sx+OvPpfenRcp9r1W0/aXwQm3z/LWfv5F5XI9rbzz2r4FLrlU8/3fw8f+rePQArSNsrBjcgYy8QiZ8nI7TpXG5xIUCjFiTSosI8eV5pXs0VouZxY+35+dLZVhVKK3SWPxE+//X3peHR1Vkb7+3u0kIS1gUFAGjgAKBhAQEV9zGUXQUGCbEkA9RB1GGYWAQFVwQmXH8qYAIJIGADMMiIjJ8jhMg8M0oSxYiS9jCYnRGFoEQIEln6aTT3fX9cftUzq3cXhKCBn85z5On03epW1XdfarqPW+9B0fOFmPiJ7mS/2x3OGGxauhxXc0PTwiBpFGx4APcx88NwrcXyvHXp3XlxV7XtcaAG9uiQ+swzBzaG5pXEZIom7d2bI014wbB5fbgNylZSH2qP1qGaBjz192AZkHq6P4YkZKNCmc1Sqs8eObuCOw9VYQyb1ao8St3o9LpkhRJSqJ9/Jwdrz/WS8IScxKiER5qxZyEaFg0De+O6IvjhaVYPXag4ct7uqhcV3msdKJP5zaoctXQ4rq2b4UvX7oXLw/pifyCUuSd1SWAC0sdOH6+DAsTY/Gvlx7Av14cjCXP3AaPW5dJnhsfjd7Xh+PIuZqEIPTFfXt4pIRdyMk7vSkbPR6B+aNi8I/f34W5W/Mx4MZ2sFkscl/A5sn3SOlnAFg8egC2TBmMlNE1G3+o3l+9dB+WPztIPruq2o13RvRB0qhYLP/tILlvId2buJogB1o9dGgdhrnx0Yj0yleQcyFHTtx8SjxCTp5LJnBnaHc4a1Etq6rdOFpQinkJ/fCP3+s5aDn1kPBz3n82m0UOCKSjU1ntlhx9GqRpdk9G8NF5uwMvrNprkC9WE4hwyijFAzi3ngYLMwrjvPh+ckDgZfuboarnzOiXHI6qjzyB2QrAnzOl/vI34w+mnEB1ClReY5jZNwpHf+JiBfacLMKYj3JQ6hSg/avz/pWPgTe1Q48OLfH9hQpvAm4vn3xpDrq0D0OlB2gVasXs9OModQrcfE0LCQNNXXcQc0dGI8RqhQV6Y8uqBZ5evhvlLqB1cxte//th/J+Pvka3a1sibnE2fvnBDhw5W4xvL5TrImSpObilYyscL7Bj/2ldFiC/sAwJqdk4VlCK0kp9lqdpGlo3t+HvE+7EvK35sFiAQ2fKoAF44eNcRHdpgxCb3t0Wmw1HzhTjgTnbMX7VXkzz6ucLAEOTMvHQ3G1Sl54cy9GCUv0aASQuzZHp5s7bHXgyNQebJ9+Dru1bYe7IaDw6P0On9Xk37by07iDil+Sg1OnBmnG3Y8qnBzAsKQvrXrgd4WEheHn9Ifw57RjslU58feISxq/ei5fXH8L7I3Ud/clr9NkvzU5f+7950nlSarsQ7+BgseoYf+LSHHgg8JcRfXHsfCmmfnYQJRVOzPriKA7/oKc9zDtbAnulLqU8Yc0+iUWTM5v5xVEANY7rvtlfyT6zO5wYmpSFsX/bLeEUohFO+Hif1Gx/ef0hQBhTCQKQm6q4M5+y7oAOl50vM8AwNJMfMj+jFjRGGkAUmFcTVlP9ORRESW24jg45UsL8XW4PhiVny5gAlUMOW9Wf53UiaIbr7fCMT+Tg1c1JdK8Zvq8Gh1V+OX/Pn0/nuanyxXV19nW5NpBEQkM54UYP4wRDzbnSf+0jeklp4uiZ6ZI2mfdDkSgoqRCxf9oqck9cFAmpWSJhcZaUq01IzRLRMzdLOmLcokwR9eYmKc1LlLPRH+2SssFRb24WO48XSNrimGU58n6iIBKdU4gaOd+ExVkiITVLOJwukXvionzGyYulIiE1S8oaE9Utv6BEJKRmifyCEpFfUCLpjLy+JH08+qNdsj4nL5aKxCXZBnol1YHkfxMWZ9WS4vUl/0uvuScuSungMctyZNu5jK7D6RIJqVkGCeD4RZkiZtYWQ5kqtU995fLIKo2TztM1ZvLHRIVUpXdJrprqTd8F3nb63M3okPy9amp/mp1XqZNm//syTuc06ze1LH8URn/3mNXVVz1U2Wi1jmr5vtpv9p63U6Uaqp+H2TOvhF1JSWZ/5TUGmeKf3MkLr6MnRxufkqk78sWZkhueX1AiYr267QmLdEdE3Oic7wqlU8gvKJEcYv4joff5BSUi8vU0ETEtTfSdsUk69pi30g3Ok2unk5OKT86UzjliWprIPXFRCCEkJ13VKS8urxJxSTp3XuraL90lRiTtlO3q/6et0uknpmbLfQKcox8xLU06e+LMJ6Zmm/5Y/DkAzuWWTn2RXif+Q84rSzEAACAASURBVKa20/NUDXZfpjpSqn/MrC216ktG9eE/8oKSCvlZq46IP4smACpf3Kwu/I9rzPPrzbjuvCz+feLl0GAWrLM3+9/XMfVzNas3d5T81R+P3d+gZ/aevgP+uOGBOPX+zql7Ffzd52uA9GfqZ381WLD1DNbRNwroBgAmrNmHh+Zuw9cnimCvdGLOyGjMjovCLz/Yjhkb8tDz+taocnmQ8/0l/G7lXuSdtePRD3cgfkkOfntXBB6etwMjkjN1upzLjQfn6gyTkgonHp63A8//bQ9+nZQhVSMrqj34/Sqdg37rda1xxEtfrKp2Y+7IaKQ+NQCFpQ7c+/52ZOUXIudkEf57oQyjluxCVOfW+O0KHUOduCYXLo/HwMYAgOdX7sHuU8XIPXERk9fsx9Fzdrz6WE/sP12CKQ/1QHhYCFaNHYgnU3OkBvuUh3rgVwsyMeWhHnh0fgaqXB5YABwvKMX4lXsxbuUeTFl7AIfPFBu20BPzh5u6ZCRWCV1fWOrAkXMl8AghGTZT1h3AO7/uI3d8ApAiXbxtKv566lKZhF1eWLUXY1fsxi8/2I7HPtyBz8bfIVk8fOlPAVFdw327pPu9vP4QencKR3jzEAPNkvPJCdde98LteHn9IdO0eQRNjFuxB08t2SVlElR9GN4/KrxDOPJ5uwMPzNmGcSv3GDBxyhd78HSRgY7oywimMMOnOSOH6qXi4xyXJ+gOgGF/gpkGDm8n1YPMjF6pmkcIKe/gCwbxl7TbHwNnwsf7TPcxmEEevI+ChUM4Q+jHVKW8XMpoQ8M9jYJ1ExPbX2zdnlkjQ9C5DQ6cKcH68XcibvEuxHbRt/+73B4cPl2EZs30YOy4lXtQ7XIj9sZr4IGAyyOw5/si5Lz2IOyV1XgyNQefvnA74hZlo3encMx4vDfiUjLhcAF9O7fG0bNl+OqlezF9/WHknS0BhK6NT3K+bVqEYP/JS3hmuZ7/tdd14cj9oRgtQ6z45Pk78F76N3BW60563pP9DIyGiWty8exdEXhu5V5sfXEwZv3zKOaOjMYvP9iOsio3botoh/zzZVj93CB0btsCEz7eB4um4Q8PdsegbtdKZ3rPu/9GZKc2CAmxSprgQ3O3Yee0XxhYJA/M2YY+ndvITULkHApLHejQOgzP/vVrWDRdQ2Z2XBQenrcDgIY14wbhvfRv4HJ5MGt4pEFW4J0R+t6CLVPulYwWwsG/8vbPebsDd737JQbc2A4rxt4OQKfn/f7jfdh9ohibJt2N99K/kYwTQMfLKTVfiM2Kt4dHItRmlQMK8d/HrdiDufHRCA/TE7MQjZIYJFxGQZUGoI1Yp4vK8dAHO7Fp0t2YszXfwIun5NzULpXpwmVu6fPgcsGAV+Lg0wNY9sxAeY8/XJjqxwdQjo9PWrsfHiFg0bRaEsFUL7vDiUfnZ+BzheFj9jzAv+RwMOwQta/MylClC/w9Q2X5AOb9ZlY3VTo6GPuxGDD8eZcrxxBsna8qCYSwG26RkMxjH34lElOzJcRBODbJGnR/NU2MSN4p+r2VLoYu3C4ivFv2CZohWQNKQ0evcckZIi4pQ0RMSxORb6TJbfq0TCJ4hyAHWlKOWZajxwMWZ0lpBCqf8Pz4lEy5fKel7vCFO0S/t9JF3xkbDcv+/IISEb8oUz4n9k9bRXxypohL0uUVery6UUIlBSUVott0PVaRX1AiJQa6s2uo/lw2gZaoJy+Wipu8sY6YWVskRu9wuqS0Q+ysLSK/oEQ/75V8IHhITSVIzyJsnUxN90d9E5ecIVPtEcTFJREIEkhcmi26v5omYRiCIBKXZotYb3yAX+8LjuGfJYcv4hdlGj5PFfJQIR0z6IPKVKERh9NVC/7yZ7wegeQVfN3Pv9v+ruX3NARkUR94xuzzCRamCVRWY7Yfq464mjD6qH6xIvZPW8W/886KiGlp4t95Z0WsVz9m9FI9kEq4be6Ji3ogzqtjQ3i5ns81XerAcPyXsO7HF2wTEdPSpK4OORH6AhWUVIjRH+3SdXIWZxmClnk/FIn4ZKNDJ5y731vpImFRDcZPejT/zjsruk2vcWDF5VWi74yNInpmusSt8wtKRL+3tsh6cw0Zh9MlRiTtlO0iZ0/xAbomMTVbdJueJhKXZtdyFpRflgdESROFNH5oUODBTgqiqk6d7jf7oXKsmxwg6c1QmWYBPXLOvHw67gu/p7JUx6jWgQYYuob6XX0+b48aMKW8rFxfiH9neAzCX1yB/88HRjoWrBPjbQv2Pl73QBZosKmr1cep+7IfI2h7NVmwjr5RYPTNrDqveFnm92hpA/p2aYN//P5OfYeoN2tP+h8Ho8rlwfCUbF13xmLB6940fGOW5WBufDQ+ef522SCeBi7UZsXtN7WDBRpah2h4/zfRSPwoB92vbSmX67OG9sbL6w9h7shofXeopkka3LQht+KJhZn4+mSRLl/sXVbZHU7YmllwS4dWsFhrcOgOrZpj4I1tsSzzewy6ub3UmCksq0SpU6DbtS1gsepStbO+OIo14wbBZtFQ5fJgplcHh5bL3xaW4aZrWwAAQm0WzBraG3GLsw05Y+cl9EP/rm0NOU5fWLUXY5fvxp/TjuHUpTIMS8qSaQNJZfGdEX0QYtPx+qFJWRi/ai+GJWfrevZeTX7Kjcuxby5lS6bu4qTdt0vH3IZQL5d86dO3GXa+cmogz8XKd6Py68PDQnBLR10S4/CZEkz4eJ/E3s0wTdJ+93jhSeK8U8pDThukY9Q+wsArq3UpiM2T75GfDd/xOnXdQV1+wg+easZVJ/onb2swsrtUP5JYBmDQ8KFr+L2nLpXh3ve349vz9oDYL49tcLniyzEVggjEffd3LpAGfZP5sGBGgyv91z6il06BTMkU3aanSUofzdpoRpyQmiVu9s58Ry/dJRIWZ8nribnS762apT6HEnK+KxQR09Lk39CF20Xikmx5TeyftoqERVmG2S6fEcalZIioNzeJhNQsOdsl1ozKLCH2Cp/B0vGoNzeJuOQMydYg6iKnFPLZYr+30kV8cqaIX5xZC4qgchNTs0X3VzeKxNRsA3RCNE+Cn2imy1kUdB1ROnl9iYFjpnqozlDV/znEoMIlZjM8TrHjr/x6+pw4q0qdnat1KC6vkqstIYShf/kxrrSpMo94uWarG/oeqfXg96jlBMOM8TVTN2s7L4/awu+lFV1dZvR8ZfZjQBHBrE6uBtjmxzQ0JHQDYAqAPACHAXwCoDmAmwHkAMgH8CmAEO+1od7333rP3xSofOLRF5RUiITULIOjzi8oETFvpev88tRs6dQlHZO9H7MsR+SeuFjry054cbfpaaL3a2lieNJOkbg027CMJ8dLjp07PIJw+lE9lmTLY0IYf3j0ZeWYOf1oTl4sFX1eT6sF53BHxqVzCaJKTM2WEExxeZXEzcmobP4sahMNRjQIqHABvarUS37cF/WQ19nMwckYRIpxYPJVDset+at6ndn/vqygpKIW/m/mJNU4hNnAZOaIyBH6omaaOV1//RDoGn+DJf/f12AarKPkk43YP201heuuhDU58rpZsI7eFmjGr2laZwCTAEQKIRyapq0DkADgMQDzhBBrNU1bDGAsgEXe1yIhRA9N0xIAvAfgSf+rCuCJhZno2UHP+ETJKQrLKvHIBzvRMtSKV9Yd0hO9eoDxq/ZK3ZkjZ4phr9Tlc6c81AO/TsnGxkl34+3hkQhvrudTnfLpAeSeLkFKYixe+DgXNq+cwGsb8gxLbk7noy36zZvpQmGUSSjUZpWwztR1ByUcQck03h3RF6891hOPzNuJ27p6NWosGg7/UAwhBMpcwOcT7pQMHYIdiBr37oi+ejILlwcWiwaPW8BjEXj4g50yTyw3WsqT9gwt50lGefPkezB13UGdklnpxBtr9TSL1CZaRtMOzQmr98mEFUCNwmOoyXKbltIqtZNYRwCw7oXbMXLxLtgdTgD659G8WQ27hhgUVH4ogzD4dnlSn+Tqi/fP/grbXn7AcIznd62s1qWgb+92jUxqwkXS6FXNu8qhBRIr49fy81XVbrhcHkxau78W64TkD9Q+48/wx64wO+5rt6eqHWN2vi4Zn+h62vlLInqctRQMW6eurJP66Of8mIyaq9WCxehtAMI0TbMBaAHgLIAHAaz3nl8BYLj3/2He9/Ce/4VGYjE+TNOAnte3xvFCPf/q6aJy3P7Ol4hbtAv9b2yL3t7t9k6XG8fO23HkTAlefawnZg2LRKlT4KVPD2LW0N6Y969vsWzMAPyfj77Gfe9vx/Mr9+CRD3fi3d/0xfrxd2Bl9kkM6KpnkHrls0NwOnW8+tnlX+PhD7bjyDm7VDKktG0lFU48Mm8Hxq/W6X2T1+yXmG/eWbvMBzpkfgZeevgWDJmfgVfXH0LLECs8msDRc3bMGhaJyBvaoOd1ejtCbBbJpyYHC9RIHr89PFJi6MfPl+Ivv+6L1s1tmP73w5i8Zj/6eGV3gZqBiCQS1K3xHcN1vRchBF5aexCz46IkvfK+2V8Zcq4CutTC7LgoADrGTfouKgZNPzBSdBy7fLcB43Y63YAGdGjVHJGdwjF13UE8u/xr3PXulzh1qQzjVuzB/XO2GfjgVG/+wx27fDfun7MNzy77Wl5fWe1GlcsNTgymTFtUD8rXOmntfqQk1iTFnrgm15Tz7Wv7+sQ1ubh/zjZZroqhT1yTi2PnS2tJI/A+ovJpnwE9n/PBL1cGwIxjrnLQg8W36V66nvLvkjhaMDz2unDB64u5X+ln/JxiAUHx6DVNmwzgLwAcALYCmAxglxCih/d8VwCbhRB9NU07DGCIEOK099x3AG4XQlxQynwewPMA0PXGGwccOpoPe6WuX9KjQ0vsPVGMrS8ORnjzZpi8Zj9cHg9yT5egT6dW+O/FCpRVurFhwp0YkZKNATe2hc1iQYWrGodOl2LDhDsxe+s3Otf674fhcQvkfH8JrUKtuKVjK1g0Dd+cL4MQHmycPBjT1x/Gu3F9Ed48RM7OQ21WOXMZ+7fd8AgBj0vgmwtliLw+HMueHShVB4mvPvOLo3oy6sW7cOt1rQA3YLFpsFn08XTqw7dgeEo2LABu79YeNoueR5VErGiT1hufH4FH6JLNTyzMxPrf3YlZ/zyqa+YnZeGz8XegR8dw2Zece/7Cqr2GRBeV1boY16B3voTNAtx+0zVIGd0f41fvhUXT+f9c9ZHKAYBxK/fAoml4e3gkZn5x1LB5h8/uztsdGDI/Q8787A4nHvlwJ76YeBe6tm8lZ9r6rB4yBeKEj/dJ/rmZ8yJeOa0+aK8BNABC38iz7JmBBk77y+sPyQ1fHiEwd2Q0Qm1W2TfExVf1aMxmhpy3TmkFzXjdVS63aUpAWqlRv3LVSaCGp0+z5cvlXavPqIvSoxqcVlc5/DwNmqHKoOyrzED1vpy2X6lnNAQX/sewBuPRA2gH4EsAHQA0A/A5gKcAfMuu6QrgkPf/PABd2LnvAFzj7xnXRPSSAZ+8H4pEwqIsidVz+mLOd4V6cDIlU8Qv0v/6ztgk4pP114hpaaLPjI1SdoCw15MXS0XUm5vF0AXbxc3T0kTUjE1iRHKGiJ65WerPJCzKkkFewuLJSMKAriE8nDBlVdOFpBJivBx1ySP/aJfo80aaGJG00xCkpPvzfigS/b3BRqrLiOSdklJYXK7ru3DMlOpBGDTFD3ggmoK6lK6Qxx3UdHocc+fHzSQeON5NKQepDNoHwYO//U3q7QuH5v2rHleDkdx4rIHosjFsL4BZLMLX89VAsBnuPWZZjox/0DGzdgTC6S8Hm/b1jGDLN4tFBDrvq031rX8w9WyoZ1zJe35sQwPSKx8C8F8hRKEQohrABgB3AWjrhXIAoAuAM97/T3sdP7zn2wC45O8BXdvrWZ7+U1iKxxdk4sjZEvx5eB8AwNECO3p2bA1YdCih53WtseTp2zAnPhrHC0rh8XgwJyEaMRHtsWnS3ehzQ1s9kUhYTVKI6esPQwPw/cUKtGimoef14bBBQ6/rwxFqs0LTLMg7W4InFmTg1cd6QgiBX83fKbejT113EEfPleLwmSK89NlBmcDD4xEYv2ovHv5gBw6eKsLQhVkYt2IPCssqMXRhJm5oE4K4RdmYsHofqlxuvPPrPqioBr49r6cHXJAQI2e/lVUuDE3KxKqxAxFqs2JYcjYqnS7850IFVj83CK9tyMOktfsxf1SMnDkTtdDpdGPI/Ayd7mm1GKh78+L7ITwsBD07tsZ76d8AqKH38YTdpCbIt9BPWXcAVS63QeKW6He0bR3Q4ZVHvc+fF98PhWWV2HuqBNOG3CpXB+FhOkWRJ0wh/NpsmztBRWZ0zIlrcg1QEt1/3u7ApLX7JRxFdNm+XdrKcgh64BAKL4PvXuUrGJ7yj+Pss+OicOScHRPX5NaCNAiGUuE03h5+rZm0rz/j/aZKIfP+CgRxqLi/WT3V82bPq6/5q2dDwSf1qWdjnsnX1YJx9CcB3KFpWgsv1v4LAEcAfAUgznvN0wD+4f3/C+97eM9/6R15/NqMx3th1JJdaNFMQ4+OrRDv1YCJ6tIWM4dFYvmzg/DZ7+5E8xB9bHltQx6SRsWi3AVUuTxIGhWLzm1b4HhBKY4W2KXWO6AHWSM7t8Enz98Bh0vfen/svF1mevrnH+5Cn85tcEvH1vifzcdxa8fWqKgWMiC79Onb8M8/3AWr1Yq//LovBt3cHuHNQ5CUGAubzYLencLx/6behy1TBmNufDT+nHYMfbu0Qd7ZcnS/tiU8HoFHPtyJUJsVA29qh15enfcqr4Z7ZKdwJD/VHwNubCedcfrke9CieTN8/JwuUeDxCMyOi5JpC6ldABASYkX65Hukbjxhqu+O6CuDmMcK7Hh7eKShzyk9IJUVypbmtLdA19xxS6dOwUfaEg8AKaP7I3PaAwgPC8Gktfsx8/MjaB2i4X82HwegB35JnwbQA5wT1+TC7qhxuNzJ0YDDHQn94Kuq3TqHfrXOoSdt9vN2Bx75cCcqHNUyFwGgDzCUW5YHGLmjUh0MzzZFrwsSYmpp9gA6DLXtpfux9OnbakkBU70pQK46chpUKqvdMiOWP2dvNiCqeWzNTHXUZs4zkFMzc/4N7QjVetZHs77JzC2goxdC5EAPqu4DcMh7zxIA0wC8qGnatwCuAbDMe8syANd4j78IYHqgZxw/V4oRyZkANGga0DzUhtVjB6JjeBheGHwzfrUgE8fOliB+8S7dabjcOPRDMeb/Kx8tbUBZpUvOPqO6tMX6392JYcnZGLt8N/5TWIoj50rgcnkQYrMgpmsb/OdCOf4+4S58/NwgDEvKwivrD8HjFvj2QjnmjozGR88MRNb0B6TGSlW1G+HNQ9C7Uzhmfn4EFmgyqXTSKG9KPugzSMq0FGa14l8vDsaHo2IwPzEGkZ301UOIzYo5T0bD4sXtPR6BeU/20xkwITaZTDs8TE+DmLhkF2Y83gsWi4bwsBDpJCkNHm1GIiE3cnAURB63cg/slU6UO9145bND0ine+/6XeGDODpy3O+TsuKraKKz1xudHsGrsQLy8/hAmfpKLcSv2YMLqfbouEGoYKVPWHZCslgUJMXg3ri+sVl2bh4S/JqzeJwcLADh8pgRT1x3EgoQYzI6LMsy0J67JNWCv3BGHNrNi20v3I2V0fyxIiJEZrUJtVtzasRW+vViOt4dHSp182kBG5ZBwGXdUqoMxCz7T4MCdOT+nOnfuoFwujyE9Hxnv9+kbDmPz5Ht8ZkMy23TlT8RMtWBn9/x5wZxvaCfM68m1+utTxx/LroZgcFCsGyHETCFELyFEXyHEU0KIKiHEf4QQg4QQPYQQI4UQVd5rK73ve3jP/ydQ+WEhVkR1aYdPntdFsV5/rBeeWJiJ/Scv4am/7saq3w7E3K356Hl9a4TaapJmHy8oRbkLGJ6SjUkPdpdL9R4dw5E++R54IPDEwky4XG7sO1WMIfN2Ir+gFB6PwOt/P4zEpTlY/dwg2CwWhIRYseWPg/Wg32pd1bKq2g2PEBi/cq/cTWqxaNLJkB05Z8eUtQfkEh0Adp+8hLJKF+57fzt+v3ofOEUk1KY7KwA4fKYYU9YekEFHCsSSlVa5MWNDnpxl0iAHwJB5aVhSFj71qjmSQ+zbpS2SRsWia/tWyJr+IBZ7Z7HhYSGI7toOX710r5yFQ9NhEqJ32h1OOKvdGL1sN2bHRWHpmNuw9OnbkDK6P6K6tAWgi5OpCSQmrsnFaxvy0KdzG4SH6cHtyE7hsFj1wZDUCre9dL8MRPP0eTRz5kYzcdrxW+WqWU0AkNc3D7Fhyx8Ho2v7VpINxGfK/nav8mdRGkH+Q+NJPLipkA+HfYCa3bh6Avb7DEHyKesOyMA5z+rkq16+sivRoKU6fH/l+GoH/R9MLta6zrjryiziSWF81eGndvbB1KM+dW3w9gUD5F/pv5jY/vqmJq/oWM53hSLqzU0yqOlw6poytAORgmxxKfr1Qz78UsSnZOoCY8mZBl0PEkPLPXFR5P1QJOKSM0TfGRu9r5ukrr1MOpKaLSKmpRl2wPZ7K92wAUruJvUGfVXtdIdT16hJTM0Wfd7YKKLe3Fyjs5+cKXd3JqZmy4Aw3yRFQUPaQEbX5heUiMQl2bJv1OfxXaDqpiCqMwVV1SAn33QWn5xpCD6rpm4UI+ObntTgrRro4xuNqBweVOb30bnRS3eJxKXZ8hnUbzxgqtaFNm752oBFzzFLVkKmBpJVATRfm814UJu3ndfRrBxeLzMzC17WJaDpbyOYv4Csv9dg6twQgmT+2vlTBU/ru/mtIe7B1aR1c+pSBSav2Y/8C2VY/duBeH7VXpRVeVBYVomnlu3G6aJyHC2wA1oN1S1pVCxmDfMGbM9WYPeJIkz+RQ/knCzC/bO349SlMgDAe+nf4JaOrfDM8t0YtWQXjp4phsVihUcIVFR74PR48MTCTDw8bycenrcDrz/eCzaLhj8P76NDIzYrAA2/ScnS9WpKHXh0fgZmPN4LR87Z8fC8HZi+Xk/tRjMsu8OJg2fseP3xXtA0QNM0HD9fCnulE99cKNNz04bpOj4A8Mr6Qxi/ci/um/0VJqzeB7tDT5unSwnrlnfWjoc/2IlKpwtWqxVVrpoR/3RROfaeKsGh08XYc6oIhaU13PTKaj0NHuHXQ1iaQb7Mp9n528MjcbywFEfP2XH0nB2vbcgzzCr0GfIOnLpUZphVEyyyICEGVS43HvXuQyBTA312hxOHfijGhI/3YdLa/RL7HzI/Q+rO8BkyaeUse3qgHsSudOLe9/W0gnQ9tYn2JyxIiNFXO8nZmLgm15Dom9dbfa/maeWwirrBiqA0f3AQUR7V9HukGQRActR9wT9q/QheM5tZ0+pKnaWrsI/ZpjGe40CtC3+OWeDW3yzU10avupja9mCffaUtmDb95MHgYEaDK/0XE9tf15NJ1jMyxadkGlQVxyzLEYlLsyUNkc8Eo2ZuFtEza2bccckZUt+FZlM0U+TZoWi2TuVwpUaajdKISlK6PAsUlU00SHX0ldmrGDXS4XSJuJQMuSWfSxxQmkRSe6R0hpyCyVcZvH4Op54qkEsvqLNMdUavUhz5bE5dXaimrgr4jFylY/qaLY5ZliNTAXLJBlU2whel0eGskWZWZYNVWqa6+uFl+co4xc1MhoHP1onuamaBZqBqv/u6V30+/9zMnsPpj3R9MHRIs1k+P+5rdRKozg1l5AOClZVoaPpnYzNcTTLFAwYMkFzvhNQaZ8eX8/Se4BJ1We5w6iJcUW9uqiWkxR025+fz5TzXruHGNWkoXWGP12oExMYsyzGIZglh5JmrEIbqqCkNoSq7q8rYckhHldqlHzEXSuMCZ2aORH1OXY0cAYm7qXo/ZL5486q2jSpBrP6Y1faqDp7Xyx8/30yrJph2qg6Q96WvVIv+4Aqz8oKFNgJ9bupnYablE6h8Kocfq09dG9Ko3eQfgrn+cuvZEGVcSbvqHD05asLLpfP2apqTg4xhzoBjp8XleuKRm6elGQS8yIkUl1eJGC/WXlBSIXq8ttHgxBNSs6Q+vRA1uHaP1zaKuKQMkZBas3GLa7vz3KV0H9deV39gNFDRD4fqyh09XcsHOmonF+ki4wMCDX48wQcdD5Rco65GP3g+cKkDCx8EVMfNVwYOp574nGvY+5tdmtXdbFBQLZhZoK/yzO4J5Aj8DTjqyrEuA5Cv1Rbvc36MygymvmYJVgJh+GblXAmr63e2aUbfiBx9//4DhBBCzur7vZUuA470N3rpLj3YuihTz37EEneQs9V30W4xOEFK/q0nDk+XycNJDZI7Zu546ItNMsRxyRlSPll1QqqML9WX2sMlj6NnbpbH+CBBg5nqDPmKho5xmWI6pv4oufSu2Y/TDI5QrzF7rxqfmavl8AGIl0UDOLVdhV7MIIxAs3ZfM+9AjtzfjLsuM+y6GH1nVIccbN2p/b7gCzPHrvZzMO2py8Cj3t+YZ8E/J7uqHP01N/WSDp1S3HWbnmaYuRMsQZg2pfXr/mqanGXTdUIYHU3MrC0iYZGekYo05WPY7J3PvPmMhs6Rs+crDe5M+cyZns3TH3KWSsLiLBlvEELUqq/6I8svKJGyCFRGv7fSa8EFZktz9Ufta3ZfXF4lYr3t45CRmUM1K4tiFyoUxgcAs7rxPud9pw5OHHqgP7O4CB9ozfqUX2PWP2pZwTgq9bNXn2NWR5WNox43u8/suf7K5v3IP5f6Wn0Gs8u9/koOFD+XQShYR98oWDdd2oZh0tr9mLBaZzt0btsC/bvqXO0Dpy7hobnbMGXtAcyNj0aIzYp343RZ3FlfHEVsl7ZweTx4Zd0hPDxvB6auO2jY4l7lcmPLHwfDZqMNSh68HxeFWzvoSZVJ0XDC6n0Yt2IPAJ0DXeVyS0XEGRvy5PVcye/l9YfgcnkwYfU+ybwAvLtH3R4MTcpCRPswvPH5ESnQZbFo8t6SCqeUFqhirA2q/3m7VyVRrwAAD7hJREFUA/GLdxn48bq6pgdT1x2UWaYqq91ypyhnHVB5xER5wKv+qLIsAOiKmLYaRsz9s79CYalD7oLlVlJh3Pxjs1kkH5y4zwsSYiQfndgaPGsTV3WkMqnvuFFfU1Yl2sBGpraX2knljVuxxyBdwDcV+dpR6u+c2g/3z9lmUAHl95vxzFU2jtlxeu+rbsQwMjNKdM6Nfy71sfowWurCGDFjJPljHV2uNRYO/o9pjcLRny52SGlcALBXOrH3ZDFe/PQAyqo80LyKheFhIXh7eCTe+PwIIIC58dGwWSzYd6oYLo8HfW5oI5UJU58agNlxURjilR54Z0QfHC+wo6waOFtcid0nijB+tb4RyuX2AIDcqGR3OHH3e1+hyuVG+uR7AAtw9FwJfvlBjXOjZ8xL6Cc3A3Ga3+KnBuCLiXch72wp3h4eKZ0fBKRDJb2Yqmq3dE5Eu/MIgVCbFb07haNDq+bweIS+C/XTAyh3evDqYz0lhZE25VAfqj8aclq9O4XLAQOAlElo0yIESaNq+g3Q93e9sv4Q8s6WSKdFFtqsZvMP6Z6EKlS3KesOyPbRK22MosGHdj5S+4n2yamqvK+XjrnN8CyzFIKhzfQ+M+jne2ms3Jn6o/sFIxlA9dryx8GIYlo6tLGKHCvf2cn7nRs5tmBVGAHUqjudox24pCUUaOAIxsz6qiGdJC8/UBvNJKYv53n/ayyYaf+V/usXE6svx1nQldQWSQHS4dSDjDHe48R0OXmxVOL2FKwUogYaGP2RDv0kLMoSuScuiriUDAnhED2PJwEn/JQHGAn2IRhGZZLQMTWd4MmLpWL4wh0GGIiyQ1FAljD4hMU1zB21fML8qVyeVYvuUSEg/j+1id9DaQYp6TXVnbNgHE49RkGxAw5bqc9QA3i8fdRH/qAkorYGgx8TjGcGd5lBPyqUw8sKBEsFglQ4dKMGQlUoxYzxQzCUvyxe6vP8wU1CmLOAgsXlfR3zBzM1tPlrX7D99L/FcDVBN9+cL0NVtRtJo2JlUupbOrTCe+nf4IuJd+GdTcdhdzgxdGEmXB4PEhZn4esTl3C6qBxDF2ZKaQJ7pT4TP3WpDC+s2otJa/dj7shoTP5kP7K/v4ThKdkor67G3pPFADSMXLwLAKSSI6lJ3v3eV5i+vgZSOXa+FDabngT88QUZuH/2NoxbsccA0RSWVeLrE5dgd+gz7MHv/Rv3vr8duafteP5ve+SM2O12o7CsEkM+3Am7wwmX24Mpaw/gWEEp7A6nVGAE9JnH28MjEdrMapAFOHq2RGZwotn29A2HDQJXtFFq3Io9KCx14I53vsT4lXsNm5NGLt6FVWMHYvr6w8j5/hIKSx0GxcuqajeeTNUTrxMMA8Dn7IsggqpqN4Z8uBNV1W4sSIjB28Mj8eDcHSgsdRgSTpPuDACD8Jgvq6x2Y9zKPXhk3g64PB55XBX3UlUVSdGSkl4TlKUqWAKQkgk8IYraXrqeoL0Jq41JxAkioZkjwXLqKoFUOi0WTer9+NKq4X3ma1ZLdSQ4kN/rbyZsBmXwYypUV9cZcV1n4L7K9SVP0VisMdZJWjCjwZX+ax/RS27zpxkbBT/5rIvLEcQlZ8jgZqCZbUFJhYiemS5+NX+7iJiWJh5fsE3OwslUXrQ6m6HVQeKSbMN5Ok71pmP93koXkW9sFBHT0kR+QYnczBQxLU2XGPDy+fl2/tFLd4nomZsNUg/dX02TzCKqGyVIp3b6euUrpO6vbjTQQinwzROw837jqxsyf7NfbvRsXzN8dbVSF3oireJ4MNYs6Mqv56sVNWiszrBVumig+qgBbLUtZnRHtSyz1ZFZvXg7/c2mzfos0H6JuszofV3vq9yGnv03xkDqlV7l+DJcTayb9jf2EgmLswzOiCAX/oMm53TyYqm42etA+7GkEv6M4JHu09NE3xkbpTMVovaS0NeXnsMP6g9RNeL0d5tWk5i6oKRCdJueZti5y50eZ/dQuSOSdop+M9NlO4lF5M9x0P+8nkQn5efJ8fmCPzhkZGaqA+Km0i35M9X6mjkmtXx+jjbN+LuO1yNG2R/B26k+P1CbffUD1cHsc6kr1BCM0/WXIDyYAepyzFd/BztYN1QdroRdTrk/xQAUrKNvFNCNpkEGL9/4XE9ePXdkNKauO4hDPxTLICdBGtyEELBXOg3LTDMLDwvB8t8Owhd/uBsWiwUetzCwEyjBs1nOzcpqN55d9jUemKMnHBm3Yo9hKWy21OzSriX6dQlHTNc2UsI3PCwE/bu2RZd2LWvlR/V4BP6cdgyfjb/DkDj828Iy3NKxFXpfrwcYKQCoMijUenPWSEmFE0+m5si+ofOkFglAasLTfXSc8uKamS/FxMpqt0zEQa8EH3C4hgcK+b0Er/iCCCgwSywbf1ACqWfyHLtkPI8rX3YfPlNiqnTpC1IhaIZr6fD21JXt4itATOXxPMO8Hv4UJX0FluvCavGllcOfbwY9NWTQk37jV4I1c7lsnMYc3G0Ujr5r+xYAgMSlOXBWu6Vw2dz4aPS5oQ2AGnyOWCvhYTaE2izQNA3T1x+W7I37FaobUINR2h1O/CXtGNxuDzweYXA6hJNynJmzTWzNLBh4k55wxGLR86j6+2ALSx3IPW3H3lMlMgmK3eFE7uli2B1GydvmzaxY+vRtmB0XhSdTcyRtEtDpoBaLBpvVItsydd3BWngqlwtWHTBnydDz6Dxh9oDuFHl2qdBmekITM8yX11394VdVu5F31o7CskocPlMTT1CzEqnMIHKa3NGaORM6zrFjfxLC1HfcqM6hyn2hzazodV3rWsd94dg8yTlRSbnTbQiWiFm97ZXOWpMNHifxxezhSV7MaI2+HJ16Ti0/WJnkyzHqb/5db0j7ObNxGoWjP1VUAXtljXOe+tlBuFx6MgkIGBzy0YJSAECfG3Ted5/ObWQCDgAGLXduHo/A5E/2o9LpQlm1gEcImZiCznPq4ZR1B/DuiL56QhNvoHj5s4PQpoWeEGRYUpbfbEBd27fCOq++/iteznx4WIg+WITVnuE1b2ZFx/AwbGaOtcrlRrlTwGLR8M6IPmjeTNeeP3i6yJBMg0xNu0ezzObNrLUcF/8yU/ISuoff1zE8rBZF0N+MEdBph+mT78E7m45jyx8HIykx1lC2Wo5az76d29SqLz2D/xBDm1llekJu6qrGV8o7qg8/X1XtxnEvOcDfs+lYUmIs+nq190mBEoDpINJQVljqwINzdsDpMv8MzJw87T/wCGFIvqJy9/050GCyVDWUs/Q3UNCejSthP0cnD6BxYPQxsf3FmGU5UlOdB824NIEQwpAkm9P2hBBSYsAMEyU5grjkDDE8aadIWJRloOiZJaKmMrl2DZUVDB3Q4TQqLHIKptm1Zs+OmbVF5P1QJHV4CkoqRATD/c12WfJyAolQ+TrGy/WXdNqf+QouquWo1wQTNJSfy0e1ZSMuV8fHl0hZoDqp/5u9v1zjAeO6fA48FuMrZuAraKt+f6504NFfDKChEpL/XAxXUzC2f/8BkpHCGQ8Op0uMWLjTwGroN3OzSEjNEtEzN+vBS2+QUghi12w2BN74HyUbiXpzk4hLzqgV0DMzGnjikzMl5z3GK0Hg74sng4ZsMOE8en6dP+dE/H7Stzl5sdS0n3ypMpoFRdV6mrWft4/3UyALdhAL5j6za7jEgpmEQqCyAj2nPs7kx+Z1+wo6m/2vHgs08PrSEKov6yZYq0tg92py9Fe6rsE6+kYB3Zy4VG56/HRROfaeLsG0IbfKJZVmsXiTglgQarPIDH2EXYO9J940ccm/OV+GMqcHXduH4ZvzpZg7MtrvzkHCgAvLKrH3dBGmDbkVoTYrBGp2t5oZLYtnx0XVpLnzAy9UVbsxa2hvAMa8n5XVbsz84ihmPN4L+YXlqKp2I9RmhU0mRDFfLnOMmHBjDn/xOnAsmaCokgqnnhS82m0IfPL7zNpshvnytvs6r/aPr2W7iqtXuYzJPszK4uXVNdgWzHWq5EJdrC6BUG5q+7i8hVl9KIDrL8BN5aoxFI7917WewZrZ5+IPQrla5AsaVXLzYEaDK/0XE9tfh2O8afNoFlFQUiHiUjIMswkuEMY57EIIyb/nMz26ZvTSXSJ+caaIfD1NRExLE1FvbgpqiU5lxS/OFDHenbHE3RfCfLclLa2JO8/PJS4x6sTT/gEum8xnXcTB51r5Zrr5ZvVQd9j6msVRvVS9+ECwjtkxfr+/pB7+lufBqE6aqT+q5msnbyALBDmp19ZlxcPvU9MQ1hcOcThdhhSLZvWpb/18wScNDaHUpayf6rl1LfdyheSCMVxN0M01Eb0k3k5YNC3P6QtMP+5Y5sgp2xM3FbvmDp8Gk7jkDNM8r2bGB5HEJdkSNuKQjPqDoHruPF5g0I53OGsSjfB71Q1L9Ep6+Crv3mx5zWEiIcy34/syqlew29wDcfhVaMVfOYGgHrNz/uISdE1xeZXhu1IfC/Y+fz9mf2WYYd/1Nd7OhnJeVxN8EsyAbHbsp5ByaEgL1tE3Cugm4hqdV96hdZiuoujli6c+NQDz4vsh9akBqKp24+X1h9DTS33TufUuPLYgU+aHrfReA0BSvYhtQBx8m9WC5iE2w/N9Lek5tDF9w2HMe7IfbFYLIju1MTBJ1KVweFgIul/bEk8v343YLm0NLBsPhJQH4MvijuFhhucR4+XLqfeiS7uWfvuPqKdcJqFNi5BalMpAS8hQ5RpfND2zLeh8uT9p7X54PEIeN2Ny0HV1pR/y/jbL/8rZNvRdqi+TIpj7qB3BUBLVa1QqZH3rSZ9JoGvqav6kCH4qdkow/Rzs+StNp2xMDB5NHxR+4kpoWimA4/QO8FZKs1ibdYjoW33hZJ6t7fU3uYrPfW9re/1NrqKz/7W163Szq+T8iWbXdI2sLvz+MITHDWiarf0N3V2Xznynl+F9T9dfOvMdAMBisTa79sY+1RdO5sHjdtV6rlI7QFwDaBcBIaBZrDVl+eo8TbO1v6EHoGmuojPf6XVj5WmaxXiM38rLB2raA9Q8r1ZdrwVwwXcbZJ26m9db6SdDf/nsEz9fHL2Nsqy21/d0FZ09avZcY7sC1dOf8fuozED1BCD7rr4WqL5Uh/q2K9j6+XtOvZ/dgPVrCPP/HfbRtiB+Gz+pXW7/RQghOgS6yBbogh/JjgshbvupK+HLNE3bI4Snkdevsfdf46xfY64b0FS/y7Wm+unWKKCbJmuyJmuyJrty1uTom6zJmqzJfubWWBz9kp+6AgGsqX6XZ425fo25bkBT/S7XmuqHRhKMbbIma7Ima7IrZ41lRt9kTdZkTdZkV8iaHH2TNVmTNdnP3JocfZM1WZM12c/cmhx9kzVZkzXZz9yaHH2TNVmTNdnP3P4/WGg9cPaP6DsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.spy(X, precision='present', markersize=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ff9a2b05cf8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.spy(Y, precision='present', markersize=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.045632721336584775"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# density\n",
    "len(X.data) / np.product(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "U, Sigma, V = sps.linalg.svds(X, k=10)\n",
    "H = U * Sigma\n",
    "W = V.T * Sigma\n",
    "\n",
    "HA = sps.coo_matrix(HA)\n",
    "WA = sps.coo_matrix(WA)\n",
    "\n",
    "Xorig = X.copy()\n",
    "Xorig.resize(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = Dataset.load_from_df(df=test_df[fields], reader=reader).build_full_trainset()\n",
    "i, j, data = zip(*test.all_ratings())\n",
    "Test = sps.coo_matrix((data, (i, j)))\n",
    "Test.resize(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Model\n",
    "\n",
    "we have now decomposed the matrix X, into two components H, and W, which represent the users and items (movies) respectively. The trick here, is to now imagine this as two graphs: one in which we relate users, and one in which we relate items. We will allow a deep learning model to traverse these graph features for each H and W, and update them.\n",
    "\n",
    "**Figure of separable Recurrent RGCNN**\n",
    "\n",
    "The features of each graph, H, and W, are the remaining components from the svd dimensionality reduction. The nodes are the users, and items respectively.  The GCNN convolves over the matrix-graph, and outputs a graph $H'$ with some number of channels/features and the same number of users (nodes).  This is then fed into the LSTM layer on a per-user basis.  For each user, the LSTM outputs a shift $dH$ which adjusts the scores for that user's components. Rinse and repeat for W.\n",
    "\n",
    "The trick here, is that we limit the number of times this iterative process occurs to some $T$.  We'll call this the diffusion time.  By passing $dH$ through a tanh nonlinearity, we allow ratings to \"diffuse\" around slightly, limiting the maximum diffusion.  After $T$ iterations, and this constitutes a *single* forward pass, and loss is then backpropogated to the parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine(H, W, minimum=1, maximum=5):\n",
    "    Xpred = torch.mm(H, torch.transpose(W, 0, 1))\n",
    "    Xpred = minimum + (maximum - 1) * (Xpred - torch.min(Xpred)) / (torch.max(Xpred) - torch.min(Xpred))\n",
    "    return Xpred\n",
    "\n",
    "\n",
    "# for computation of loss\n",
    "def frobenius_norm(x):\n",
    "    \"\"\"norm for matrices\"\"\"\n",
    "    x2 = x ** 2\n",
    "    x2sum = torch.sum(x2)\n",
    "    return torch.sqrt(x2sum)\n",
    "\n",
    "\n",
    "def graph_norm(X, laplacian):\n",
    "    norm = torch.mm(torch.transpose(X, 0, 1), tsps.mm(laplacian, X))\n",
    "    return norm\n",
    "\n",
    "\n",
    "def recommender_loss(inputs, laplacians, target, mask, extrema, gamma=1e-10):\n",
    "    # loss function borrowed from objective in Srebro et. al 2004.\n",
    "    H, W = inputs\n",
    "    Lh, Lw = laplacians\n",
    "\n",
    "    # set X to valid ratings\n",
    "    X = torch.mm(H, torch.transpose(W, 0, 1))\n",
    "    X_normed = extrema[0] + (extrema[1] - 1) * (X - torch.min(X)) / (torch.max(X) - torch.min(X))\n",
    "\n",
    "    # consider only original data and test data, ignore other sparse values.\n",
    "    xm = mask.to_dense() * (X_normed - target)\n",
    "    fnorm = frobenius_norm(xm)\n",
    "    fnorm = fnorm / torch.sum(mask.to_dense())\n",
    "\n",
    "    # compute regularization\n",
    "    gH = graph_norm(H, Lh)\n",
    "    gW = graph_norm(W, Lw)\n",
    "    loss = fnorm + (gamma / 2) * (torch.trace(gH) + torch.trace(gW))\n",
    "    return loss\n",
    "\n",
    "def tensor_from_scipy_sparse(X):\n",
    "    values = X.data\n",
    "    indices = np.vstack((X.row, X.col))\n",
    "    i = torch.LongTensor(indices)\n",
    "    v = torch.FloatTensor(values)\n",
    "    X = torch.sparse.FloatTensor(i, v, torch.Size(X.shape))\n",
    "    return X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RGCNNFactorization(nn.Module):\n",
    "\n",
    "    def __init__(self, input_shape, factorization_rank=10, n_channels=32,\n",
    "                 basis_order=5, diffusion_time=10, hidden_cells=32,\n",
    "                 lstm_layers=1, bidirectional=False):\n",
    "        super().__init__()\n",
    "\n",
    "        # GCNN parameters\n",
    "        self.m = input_shape[0]\n",
    "        self.n = input_shape[1]\n",
    "        self.r = factorization_rank\n",
    "        self.q = n_channels\n",
    "        self.order = basis_order\n",
    "\n",
    "        # LSTM parameters\n",
    "        self.nH = hidden_cells\n",
    "        self.T = diffusion_time\n",
    "        self.n_layers = lstm_layers\n",
    "        self.bidirectional = False\n",
    "\n",
    "        # GCNNs for H, W matrices\n",
    "        self.hconv = gnn.ChebConv(in_channels=self.r, out_channels=self.q,\n",
    "                                  K=self.order)\n",
    "        self.wconv = gnn.ChebConv(in_channels=self.r, out_channels=self.q,\n",
    "                                  K=self.order)\n",
    "\n",
    "        # RNN\n",
    "        self.lstm = nn.LSTM(input_size=self.q, hidden_size=self.nH,\n",
    "                            num_layers=self.n_layers, bidirectional=self.bidirectional,\n",
    "                            batch_first=True)\n",
    "\n",
    "        self.dense_H = nn.Linear(in_features=self.nH, out_features=self.r)\n",
    "\n",
    "        self.dense_W = nn.Linear(in_features=self.nH, out_features=self.r)\n",
    "\n",
    "        self.loss_fn = recommender_loss\n",
    "\n",
    "    def init_hidden(self):\n",
    "        h0 = torch.zeros((self.q,)).view(1, 1, -1)\n",
    "        c0 = torch.zeros((self.q,)).view(1, 1, -1)\n",
    "        return h0, c0\n",
    "\n",
    "    def forward(self, H, W, HA, WA):\n",
    "        hidden = self.init_hidden()\n",
    "        for i in range(self.T):\n",
    "            conv1 = self.hconv(H, HA)\n",
    "            Htilde = torch.sigmoid(conv1)\n",
    "            out, hidden = self.lstm(Htilde.unsqueeze(0), hidden)\n",
    "            dout = self.dense_H(out)\n",
    "            dH = torch.tanh(dout).squeeze()\n",
    "            Hout = H + dH\n",
    "\n",
    "            conv2 = self.wconv(W, WA)\n",
    "            Wtilde = torch.sigmoid(conv2)\n",
    "            out, hidden = self.lstm(Wtilde.unsqueeze(0), hidden)\n",
    "            dout = self.dense_W(out)\n",
    "            dW = torch.tanh(dout).squeeze()\n",
    "            Wout = W + dW\n",
    "\n",
    "        return Hout, Wout\n",
    "\n",
    "    def train(self, H, W, HA, WA, Y,  Xorig, Test, iters, optimizer=None):\n",
    "        if optimizer is None:\n",
    "            optimizer = optim.Adam(self.parameters(), lr=1e-4)\n",
    "\n",
    "        loss_history = np.zeros((iters,))\n",
    "        error_history = np.zeros((iters,))\n",
    "\n",
    "        testmask = sps.coo_matrix((np.ones_like(Test.data), (Test.row, Test.col)))\n",
    "        testmask.resize(Xorig.shape)\n",
    "        testmask = tensor_from_scipy_sparse(testmask).cuda()\n",
    "        Test = tensor_from_scipy_sparse(Test).cuda()\n",
    "\n",
    "        maximum = np.max(Y)\n",
    "        minimum = np.min(Y)\n",
    "        M = sps.coo_matrix(Y + Xorig)\n",
    "        allmask = tensor_from_scipy_sparse(sps.coo_matrix((np.ones_like(M.data), (M.row, M.col)))).cuda()\n",
    "        # Y = tensor_from_scipy_sparse(Y).cuda()\n",
    "        M = tensor_from_scipy_sparse(M).cuda()\n",
    "\n",
    "        Lh = sps.csgraph.laplacian(HA)\n",
    "        Lw = sps.csgraph.laplacian(WA)\n",
    "        Lh = tensor_from_scipy_sparse(Lh).cuda()\n",
    "        Lw = tensor_from_scipy_sparse(Lw).cuda()\n",
    "\n",
    "        H = torch.tensor(H).float().cuda()\n",
    "        W = torch.tensor(W).float().cuda()\n",
    "        HA = torch.tensor([HA.row, HA.col]).long().cuda()\n",
    "        WA = torch.tensor([WA.row, WA.col]).long().cuda()\n",
    "\n",
    "        for i in range(iters):\n",
    "\n",
    "            Hout, Wout = self.forward(H, W, HA, WA)\n",
    "            loss = self.loss_fn((Hout, Wout), (Lh, Lw), M, allmask, (minimum, maximum))\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "            loss_history[i] = loss.item()\n",
    "            Xpred = combine(Hout, Wout)\n",
    "            error_history[i] = self.predict(Xpred, Test, testmask)\n",
    "            print('iter %s: loss: %s, error: %s' % (i, loss_history[i], error_history[i]))\n",
    "\n",
    "        return Hout.cpu().detach(), Wout.cpu().detach(), loss_history, error_history\n",
    "\n",
    "    def predict(self, X, Y, Mask=None, norm=False):\n",
    "        if norm:\n",
    "            X = 1 + 4 * (X - torch.min(X)) / (torch.max(X) - torch.min(X))\n",
    "        if Mask is None:\n",
    "            Mask = sps.coo_matrix((np.ones_like(Y.data), (Y.row, Y.col)), shape=Y.shape)\n",
    "            Mask = tensor_from_scipy_sparse(Mask)\n",
    "            Y = tensor_from_scipy_sparse(Y)\n",
    "        predictions = X * Mask.to_dense()\n",
    "        predictions_error = torch.sum(torch.abs(predictions - Y)) / torch.sum(Mask.to_dense())\n",
    "        return predictions_error.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "model = RGCNNFactorization(X.shape)\n",
    "# model.train(H, W, HA, WA, Y, Xorig, Test, 100)\n",
    "model = RGCNNFactorization(X.shape).load_state_dict(torch.load(glob('models/model5000_*.pickle')[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVDpp at 0x7ff974ef9ba8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = SVDpp(n_factors=10)\n",
    "algo.fit(train_set.build_full_trainset())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE:  0.7393\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7392581338017533"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae(algo.test(test.build_testset()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": "2f096c1f60ea4f1b9be69b4b16765d9f",
   "lastKernelId": "ee566972-3b1a-4d6e-ba82-fe652ec365d2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
